Loading required packages

#importing libraries
library("tidyverse")
library(here)
library(ggrepel)
library(lme4)
library(lmerTest)
library(sjstats)
library(emmeans)
library(MuMIn)
library(sjPlot)
library(sjmisc)
library("car")

Preliminary analysis

Reading data into data frame

#Ethnicity data set
ethnicity_data <- read_delim(file = here("data", "qry_Ethnic_origin_2.txt"), delim = ",")
Rows: 44191 Columns: 3
── Column specification ───────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (2): GENDER_DESC, ETHNIC_ORIGIN_DESC
dbl (1): ID2

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(ethnicity_data)

#Mark data set
mark_data <- read_delim(file = here("data", "qry_Mark_Data_2.txt"), delim = ",")
Rows: 1117342 Columns: 22
── Column specification ───────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (13): PROGRAMME_SCHOOL, PROGRAMME_OWNER, PROGRAMME_LEVEL_DESC, PROGRAMME_PART, PART_END...
dbl  (9): ID2, STUDENT_START_YEAR, PART_START_ACADEMIC_YEAR, INSTANCE_ACADEMIC_YEAR, MODULE...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(mark_data)

Ethnicity Dataset

Cleaning

#checking for missing data
ethnicity_data %>% sapply(function(x) sum(is.na(x)) )
               ID2        GENDER_DESC ETHNIC_ORIGIN_DESC 
                 0                  1                 15 
#removing missing data
clean_ethnicity_data <- ethnicity_data %>%
  drop_na()

#checking if na's removed.
clean_ethnicity_data %>% sapply(function(x) sum(is.na(x)))
               ID2        GENDER_DESC ETHNIC_ORIGIN_DESC 
                 0                  0                  0 
#checking for duplicates & removing
length(unique(clean_ethnicity_data$ID2)) #number of students in ethnic data
[1] 44176
length(unique(clean_ethnicity_data$ID2)) == nrow(clean_ethnicity_data) #returns TRUE as no duplicates
[1] TRUE
#renaming columns
clean_ethnicity_data %>%
  rename(
    Gender = GENDER_DESC,
    Ethnicity = ETHNIC_ORIGIN_DESC
    ) -> clean_ethnicity_data
#count of ethnic groups / adding column for % of each ethnicity

#check levels of gender
clean_ethnicity_data %>%
  group_by(Gender) %>%
  tally () %>%
  mutate(freq = (n / sum(n))*100) %>%
  mutate(percentage = scales::label_percent()(n / sum(n))) %>%
  rename("Number of students" = n)

#Categorising into man vs woman other levels to little cases
#remove non binary, other & prefer not to say
clean_ethnicity_data<-clean_ethnicity_data[!(clean_ethnicity_data$Gender=="Non-Binary" | clean_ethnicity_data$Gender=="Prefer not to say" | clean_ethnicity_data$Gender=="Other") ,]


#check levels of ethnicites
clean_ethnicity_data %>%
  group_by(Ethnicity) %>%
  tally () %>%
  mutate(freq = (n / sum(n))*100) %>%
  mutate(percentage = scales::label_percent()(n / sum(n)))

I will categorise ethnicity differently depending on research question. Firstly, I will begin with white vs other.

#duplicating data set at this point as will categorise differently for second research question
clean_ethnicity_data -> clean_ethnicity_data2
#categorise ethnicities into white vs non white
#rename all white columns- white.
#rename all other columns other.

clean_ethnicity_data[clean_ethnicity_data == "White - British"] <- "White"
clean_ethnicity_data[clean_ethnicity_data == "White - Irish"] <- "White"
clean_ethnicity_data[clean_ethnicity_data == "White - Scottish"] <- "White"
clean_ethnicity_data[clean_ethnicity_data == "Other White Background"] <- "White"

clean_ethnicity_data[clean_ethnicity_data == "Arab"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Asian Or Asian British - Bangladeshi"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Asian Or Asian British - Indian"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Asian Or Asian British - Pakistani"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Black Or Black British - African"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Black Or Black British - Caribbean"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Chinese"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Gypsy or Traveller"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Mixed - White And Asian"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Mixed - White And Black African"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Mixed - White And Black Caribbean"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Other Asian Background"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Other Black Background"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Other Ethnic Background"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Other Mixed Background"] <- "BAME"

#remove not known,no information & info refused- as could be any ethnicity.

clean_ethnicity_data<-clean_ethnicity_data[!(clean_ethnicity_data$Ethnicity=="Information Refused" | clean_ethnicity_data$Ethnicity=="Information Refused - OBSOLETE VALUE" | clean_ethnicity_data$Ethnicity=="No Information" | clean_ethnicity_data$Ethnicity=="Not Known") ,]
#changing numerical variables to factors. 
clean_ethnicity_data %>%
  mutate_at(vars(Gender, Ethnicity), list(factor)) -> clean_ethnicity_data

summary(clean_ethnicity_data)
      ID2          Gender      Ethnicity    
 Min.   :    1   Man  :24727   BAME :17389  
 1st Qu.:11045   Woman:18025   White:25363  
 Median :22124                              
 Mean   :22101                              
 3rd Qu.:33137                              
 Max.   :44191                              

Visualisation

#Ethnicity
#Visualising number of students in each ethnicity category 

ethnicity_data %>%
  group_by(ETHNIC_ORIGIN_DESC) %>%
  summarise(counts = n()) %>%
  mutate(freq = (counts / sum(counts))*100) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(., aes(x = ETHNIC_ORIGIN_DESC, y = counts)) +
  geom_bar(fill = "#0073C2FF", stat = "identity") +
  #geom_text(aes(label = percentage), angle = 60) +
  theme(axis.text.x = element_text(angle = 60, 
                                   hjust = 1)) +
  ggtitle("Number of students in each ethnic category") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Ethnicity') +
  ylab('Number of students') 

#Ethnicity
#Percentage of students belonging to each ethnicity after categorisation
clean_ethnicity_data %>%
  group_by(Ethnicity) %>%
  tally () %>%
  mutate(freq = scales::label_percent()(n / sum(n)))  %>%
  ggplot( ., aes(x="", y=freq, fill=Ethnicity)) +
  geom_bar(stat="identity", width=1, color="white") +
  coord_polar("y", start=0) +
  theme_void() +
  geom_text(aes(label = freq), color = "white", position = position_stack(vjust = 0.5), size = 4) +
  scale_fill_brewer(palette = "Paired") +
  ggtitle("Percentage of students in each ethnic category") +
  theme(plot.title = element_text(hjust = 0.5)) 

#GENDER
#Percentage of students belonging to each gender category
ethnicity_data %>%
  group_by(GENDER_DESC) %>%
  summarise(counts = n()) %>%
  mutate(freq = (counts / sum(counts))*100) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(., aes(x = GENDER_DESC, y = counts, fill=GENDER_DESC)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = percentage), vjust = -0.5) + 
  scale_fill_brewer(palette = "Paired") +
  ggtitle("Percentage of students in each gender category") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Gender') +
  ylab('Number of students') +
  guides(fill = guide_legend(title = "Gender Description ")) 

#GENDER
#Percentage of students belonging to each gender after categorisation
clean_ethnicity_data %>%
  group_by(Gender) %>%
  tally () %>%
  mutate(freq = scales::label_percent()(n / sum(n))) %>%
  ggplot( ., aes(x="", y=freq, fill=Gender)) +
  geom_bar(stat="identity", width=1, color="white") +
  coord_polar("y", start=0) +
  theme_void() +
  geom_text(aes(label = freq), color = "white", position = position_stack(vjust = 0.5), size = 5) +
  scale_fill_brewer(palette = "Set2") +
  ggtitle("Percentage of students in each gender category") +
  theme(plot.title = element_text(hjust = 0.5)) 

Mark Dataset

Cleaning

#summary of mark data
summary(mark_data)
      ID2        STUDENT_START_YEAR PROGRAMME_SCHOOL   PROGRAMME_OWNER    PROGRAMME_LEVEL_DESC
 Min.   :    1   Min.   : 7.0       Length:1117342     Length:1117342     Length:1117342      
 1st Qu.:11013   1st Qu.:17.0       Class :character   Class :character   Class :character    
 Median :22121   Median :18.0       Mode  :character   Mode  :character   Mode  :character    
 Mean   :22094   Mean   :17.8                                                                 
 3rd Qu.:33118   3rd Qu.:19.0                                                                 
 Max.   :44191   Max.   :22.0                                                                 
                                                                                              
 PROGRAMME_PART     PART_START_ACADEMIC_YEAR PART_END_DATE      MODULE_OWNER      
 Length:1117342     Min.   : 8.00            Length:1117342     Length:1117342    
 Class :character   1st Qu.:18.00            Class :character   Class :character  
 Mode  :character   Median :19.00            Mode  :character   Mode  :character  
                    Mean   :18.66                                                 
                    3rd Qu.:20.00                                                 
                    Max.   :21.00                                                 
                                                                                  
 MODULE_SCHOOL      MODULE_CODE        MODULE_LONG_TITLE  INSTANCE_ACADEMIC_YEAR
 Length:1117342     Length:1117342     Length:1117342     Min.   :17.0          
 Class :character   Class :character   Class :character   1st Qu.:18.0          
 Mode  :character   Mode  :character   Mode  :character   Median :19.0          
                                                          Mean   :18.7          
                                                          3rd Qu.:20.0          
                                                          Max.   :21.0          
                                                                                
 MODULE_INSTANCE_NO  MODULE_MARK     INCLUDE_MARK_DESC  MODULE_CREDITS 
 Min.   : 1.000     Min.   :  0.00   Length:1117342     Min.   : 10.0  
 1st Qu.: 1.000     1st Qu.: 58.00   Class :character   1st Qu.: 10.0  
 Median : 1.000     Median : 65.00   Mode  :character   Median : 15.0  
 Mean   : 1.217     Mean   : 63.94                      Mean   : 16.4  
 3rd Qu.: 1.000     3rd Qu.: 72.00                      3rd Qu.: 20.0  
 Max.   :59.000     Max.   :100.00                      Max.   :120.0  
                    NA's   :8                                          
 ASSESSMENT_COMPONENT_CODE ASSESSMENT_COMPONENT_TITLE ASSESSMENT_COMPONENT_TYPE_DESC
 Length:1117342            Length:1117342             Length:1117342                
 Class :character          Class :character           Class :character              
 Mode  :character          Mode  :character           Mode  :character              
                                                                                    
                                                                                    
                                                                                    
                                                                                    
 ASSESSMENT_WEIGHT      MARK       
 Min.   :  0.00    Min.   : -3.00  
 1st Qu.: 20.00    1st Qu.: 58.00  
 Median : 40.00    Median : 66.00  
 Mean   : 48.34    Mean   : 66.18  
 3rd Qu.: 75.00    3rd Qu.: 75.00  
 Max.   :100.00    Max.   :100.00  
                                   
#checking for missing data?
mark_data %>% sapply(function(x) sum(is.na(x)) )
                           ID2             STUDENT_START_YEAR               PROGRAMME_SCHOOL 
                             0                              0                          20509 
               PROGRAMME_OWNER           PROGRAMME_LEVEL_DESC                 PROGRAMME_PART 
                             0                              0                              0 
      PART_START_ACADEMIC_YEAR                  PART_END_DATE                   MODULE_OWNER 
                             0                         130170                              0 
                 MODULE_SCHOOL                    MODULE_CODE              MODULE_LONG_TITLE 
                         20509                              0                              0 
        INSTANCE_ACADEMIC_YEAR             MODULE_INSTANCE_NO                    MODULE_MARK 
                             0                              0                              8 
             INCLUDE_MARK_DESC                 MODULE_CREDITS      ASSESSMENT_COMPONENT_CODE 
                             0                              0                              0 
    ASSESSMENT_COMPONENT_TITLE ASSESSMENT_COMPONENT_TYPE_DESC              ASSESSMENT_WEIGHT 
                             0                              0                              0 
                          MARK 
                             0 
#remove missing data??? - decided to not remove missing data as only missing data in programme school, module school and part_end_date and module mark- i am not directly assessing these columns- so doesn't matter too much. If looking at effect of school on mark may need to consider removing missing values. 

#checking for duplicates
length(unique(mark_data$ID2)) #number of students in marks data- same as ethnicity data set
[1] 44191
length(unique(mark_data$ID2)) == nrow(mark_data) #returns FALSE as there are duplicates
[1] FALSE
#removing duplicate rows- 1,114,911 rows once duplicates removed.
distinct(mark_data) -> clean_mark_data
#changing variables to factors.
clean_mark_data %>%
  mutate_at(vars(PROGRAMME_SCHOOL, PROGRAMME_OWNER, PROGRAMME_LEVEL_DESC, PROGRAMME_PART, PART_END_DATE, MODULE_OWNER, MODULE_SCHOOL, MODULE_CODE, MODULE_LONG_TITLE, INCLUDE_MARK_DESC, ASSESSMENT_COMPONENT_CODE, ASSESSMENT_COMPONENT_TITLE, ASSESSMENT_COMPONENT_TYPE_DESC), list(factor)) -> clean_mark_data

Visualisation

#how many years of data?
clean_mark_data %>%
  group_by(STUDENT_START_YEAR) %>%
  count ()
#data spans 2007-2022
#Only interested in the last 5 years, as before 2016, a lot less data/outdated.
#mark data only has a single students results for 2022, therefore, only one gender, one ethnicity.
#This is not good enough for any analysis on students performance, will cause serious over generalisation.


#remove data before 2017 & after 2021
clean_mark_data %>%
  filter(STUDENT_START_YEAR >= 17 & STUDENT_START_YEAR <= 21) ->clean_mark_data

#Visualisation of students in each year group.
clean_mark_data %>%
  group_by(STUDENT_START_YEAR) %>% 
  count() %>%
  ggplot(., aes(x = STUDENT_START_YEAR, y= n)) + 
  geom_bar(stat = "identity", fill = "darkgreen", size = 2.5) +
  geom_text(aes(label = n), vjust = -0.5) +
  ggtitle("Number of students in each year (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Student Start Year') +
  ylab('Number of students')  


#now only 886,263 rows
#How many assessment types in data set?
clean_mark_data %>%
  group_by(ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  count()
#Coursework, Exam, In-Class Test, In-Dept Exam, Laboratory Test

#types of assessment types & relative percentages of students who did the AT
clean_mark_data %>%
  group_by(ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  summarise(counts = n()) %>%
  mutate(freq = (counts / sum(counts))) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot( ., aes(x="", y=freq, fill=ASSESSMENT_COMPONENT_TYPE_DESC)) +
  geom_bar(stat="identity", width=1, color="white") +
  coord_polar("y", start=180) +
  theme_void() +
  geom_text(aes(label = percentage), position = position_stack(vjust = 0.5), size = 3) +
  scale_fill_brewer(palette = "Spectral") +
  guides(fill = guide_legend(title = "Assessment type")) +
  ggtitle("Percentage of students in each assessment type") +
  theme(plot.title = element_text(hjust = 0.5)) 

#fix labels?? or change to bar chart
#change to dark2?
#categorise into exam and cw only. 
#So- exam = Exam, In-Dept Exam, cw = CW, In-Class Test and Lab Test

clean_mark_data[clean_mark_data == "In-Dept Exam"] <- "Exam"
Warning: stack imbalance in '==', 41 then 43
Warning: stack imbalance in '==', 25 then 27
Warning: stack imbalance in '[<-', 10 then 12
Warning: stack imbalance in '<-', 2 then 4
clean_mark_data[clean_mark_data == "In-Class Test"] <- "Coursework"
clean_mark_data[clean_mark_data == "Laboratory Test"] <- "Coursework"

#dropping levels 
#in dept exam, in class test and lab test
droplevels(clean_mark_data$ASSESSMENT_COMPONENT_TYPE_DESC) -> clean_mark_data$ASSESSMENT_COMPONENT_TYPE_DESC

summary(clean_mark_data$ASSESSMENT_COMPONENT_TYPE_DESC)
Coursework       Exam 
    618851     267412 
#visualising percentage of students under each assessment type after categorisation
clean_mark_data %>%
  group_by(ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  summarise(counts = n()) %>%
  mutate(freq = (counts / sum(counts))) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot( ., aes(x="", y=freq, fill=ASSESSMENT_COMPONENT_TYPE_DESC)) +
  geom_bar(stat="identity", width=1, color="white") +
  coord_polar("y", start=0) +
  theme_void() +
  geom_text(aes(label = percentage), position = position_stack(vjust = 0.5), size = 3) +
  scale_fill_brewer(palette = "Spectral") +
  guides(fill = guide_legend(title = "Assessment type")) +
  ggtitle("Percentage of students in each assessment type") +
  theme(plot.title = element_text(hjust = 0.5)) 

#cleaning programme part
summary(clean_mark_data$PROGRAMME_PART)
     A      B      C      D      F      T 
343429 229228  97993   6310  18717 174344 
ggplot(clean_mark_data, aes(x = PROGRAMME_PART)) + 
  geom_bar(fill = "blue") +
theme(axis.text.x = element_text(angle = 60, 
                                   hjust = 1)) +
  ggtitle("Number of students in each part") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Programme Part') +
  ylab('Number of students')  

As can be seen from the above table- there are many parts- but in this study i am only interested in UG and PG students, so will remove all cases where not A, B, C, D, F, and T.

#as can be seen from the above table- there are many parts- but in this study i am only interested in UG and PG students, so will remove all cases where not A, B, C, D, F, and T. 
#There are 16 parts- want to keep 6 and remove 10

subset(clean_mark_data, PROGRAMME_PART != "G" & PROGRAMME_PART != "I" & PROGRAMME_PART != "R" & PROGRAMME_PART != "R0" & PROGRAMME_PART != "R1" & PROGRAMME_PART != "R2" & PROGRAMME_PART != "RT" & PROGRAMME_PART != "TO" & PROGRAMME_PART != "TR" & PROGRAMME_PART != "TX") -> clean_mark_data
#dropping levels with 0 
summary(clean_mark_data$PROGRAMME_PART)
     A      B      C      D      F      G      I      R     R0     R1     R2     RT      T 
343430 229228  97993   6310  18717      0      0      0      0      0      0      0 174344 
    TO     TR     TX 
     0      0      0 
droplevels(clean_mark_data$PROGRAMME_PART) -> clean_mark_data$PROGRAMME_PART

summary(clean_mark_data$PROGRAMME_PART)
     A      B      C      D      F      T 
343430 229228  97993   6310  18717 174344 
ggplot(clean_mark_data, aes(x = PROGRAMME_PART)) + 
  geom_bar(fill = "blue") +
theme(axis.text.x = element_text(angle = 60, 
                                   hjust = 1)) +
   ggtitle("Number of students in each part") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Programme Part') +
  ylab('Number of students') 

#distribution of marks in cleaned mark dataset
hist(clean_mark_data$MARK, 
     main="Histogram of mark (2017-2021)",
xlab="Mark",
xlim=c(0,100),
col="darkmagenta")

#seems close to normal distribution
#from above looks like there are marks below 0
clean_mark_data %>%
  group_by(MARK) %>%
  tally ()
#REMOVE MARKS BELOW, ONE ENTRY OF -3. 
filter(clean_mark_data, MARK > 0) -> clean_mark_data
#now 870021 rows

Joining the two datasets

#JOIN ETHNIC DATA AND MARK DATA
inner_join(clean_ethnicity_data, clean_mark_data) -> full_data
Joining, by = "ID2"
print(distinct(full_data))
#840368 rows

Research Question 1

1. What affect does the assessment type have on the student performance and the BAME awarding gap?

This is a multi-leveled question. I will began by looking at the effect of ethnicity and assessment type separately on student performance.

1.1 Effect of Ethnicity on student performance

Variance in marks for white vs other

boxplot(MARK~Ethnicity, data = full_data)

looks like a lot of data points are outliers- but data is over 5 years and we are mostly interested in top grades so will filter these out later.

Analysis of average Marks of White vs BAME/Other students (2017-2021)

Below is bar chart showing of effect of ethnicity on mean mark between 2017-2021

full_data %>%
  select(Ethnicity, MARK) %>%
  group_by(Ethnicity) %>%
  tally(mean(MARK))  %>%
  rename(Average_mark = n) %>%
  ggplot(aes(x=Ethnicity, y = Average_mark, fill =Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Paired") +
  geom_text(aes(label = round(Average_mark, 2)), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Average Test Scores by Ethnicity between 2017-2021") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ylab('Average Mark') 

Next, I wanted to see the affect of ethnicity on average mark each year between 2017-2021. This table below shows the gap in average grade in white vs Bame students is reducing over the years. This reduction is very small changing by just 1.4 over 5 years.

#White vs non white grades table like in screenshot in report
full_data %>%
  select(INSTANCE_ACADEMIC_YEAR, Ethnicity,MARK) %>%
  group_by(INSTANCE_ACADEMIC_YEAR, Ethnicity) %>%
  tally(mean(MARK)) %>%
  rename(Average_mark = n) %>%
  spread(key = Ethnicity, value = Average_mark) %>%
  mutate(Gap = White - BAME)

Below is a line graph to visualise the above table. This is showing gap is reducing, but both white and bame students average grade seems to be reducing over the years.

full_data %>%
  select(INSTANCE_ACADEMIC_YEAR, Ethnicity,MARK) %>%
  group_by(INSTANCE_ACADEMIC_YEAR, Ethnicity) %>%
  tally(mean(MARK)) %>%
  rename(Average_mark = n) %>%
  #spread(key = Ethnicity, value = Average_mark) %>%
 # mutate(Gap = White - BAME) %>%
  ggplot(aes(x = INSTANCE_ACADEMIC_YEAR, y = Average_mark, col = Ethnicity)) +
  geom_line() +
  geom_point() +
  ggtitle("Average grade for white vs BAME students (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Academic Year') +
  ylab('Average Mark') +
  labs(col='Ethnicity') 

Analysis of BAME Awarding Gap (2017-2021)

The below bar graph is showing percentage of white vs BAME students achieving top degree (2017-2021) NOTE- for methodology/results I grouped by ID2 first in order to calculate average grade for each student..then i filtered for students who got an average grade above 60 Then grouped by ethnicity This is important as the data is not important, if not grouped by ID2 results would show any student who got above 60 for at least 1 assessment

full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(Average_mark >=60) %>%
  select(ID2, Ethnicity, Average_mark) %>%
  distinct() %>%
  group_by(Ethnicity) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=Ethnicity, y = counts, fill =Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Pastel1") +
  geom_text(aes(label = percentage), position = position_stack(vjust = 1.1), size = 3)  +
  ggtitle("Percentage of white vs BAME students achieving top degree (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ylab('Number of students')

Below graph shows difference between number of White vs BAME students achieving top degree (BAME awarding gap) is consistent over years 2017-2021

#Visualising percentage of white vs BAME students achieving top degree each year (2017-2021)
full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(Average_mark >=60) %>%
  select(ID2, Ethnicity, Average_mark, INSTANCE_ACADEMIC_YEAR) %>%
  distinct() %>%
  group_by(INSTANCE_ACADEMIC_YEAR, Ethnicity) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=INSTANCE_ACADEMIC_YEAR, y = counts, fill =Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Pastel1") +
  geom_text(aes(label = percentage), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Percentage of white vs BAME students achieving top degree each year (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Academic Year') +
  ylab('Number of students')
`summarise()` has grouped output by 'INSTANCE_ACADEMIC_YEAR'. You can override using the `.groups` argument.

#this shows awarding gap is reducing but only by 4% between 2017-2020, 2021 less data so not good for comparison

Problem with above graphs is that there are a lot more white students in the university, and therefore it is a given that more white students are achieving top degrees. So instead, this graph clearly shows distribution of degree outcomes instead.The following graph makes the results clearer for interpretation/comparisons. For example here, 37% of white students get a first in comparison to only 21.9% of BAME students.

full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  mutate(Grade = case_when(Average_mark >= 70 ~ "First Class",
                           Average_mark >= 60 ~ "Upper Second Class Honour",
                           Average_mark >= 50 ~ "Lower Second Class Honour",
                           Average_mark >= 40 ~ "Third class/pass",
                           Average_mark < 40 ~ "Fail")) %>%
  group_by(Ethnicity, Grade) %>%
  summarise(counts = n()) %>%
  mutate(freq = (counts / sum(counts))) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=Ethnicity, y = freq, fill =Grade)) +
  geom_bar(stat = 'identity') + 
  coord_flip() +
  scale_fill_brewer(palette = "Pastel1") +
  geom_text_repel(aes(label = percentage), position = position_stack(vjust = 0.5), size = 2.5) +
  ggtitle("Distribution of degree outcomes by ethnic group (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  labs(y = "Frequency", fill = "Degree Classification") #+
`summarise()` has grouped output by 'Ethnicity'. You can override using the `.groups` argument.

  #scale_fill_discrete(breaks=c('First Class', 'Upper Second Class Honour', 'Lower Second Class Honour', 'Third class/pass', 'Fail'))

1.2 Effect of assessment type on student performance

I have began by visualising the average mark for each assessment type (2017-2021)

#Visualise effect of assessment type on average mark between 2017-2021
full_data %>%
  select(ASSESSMENT_COMPONENT_TYPE_DESC, MARK) %>%
  group_by(ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  tally(mean(MARK))  %>%
  rename(Average_mark = n) %>%
   #spread(key = Ethnicity, value = Average_mark) %>%
  ggplot(aes(x=ASSESSMENT_COMPONENT_TYPE_DESC, y = Average_mark, fill = ASSESSMENT_COMPONENT_TYPE_DESC)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Pastel2") +
  geom_text(aes(label = round(Average_mark, 2)), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Average Mark for Coursework vs Exam (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Assessment Type') +
  ylab('Average Mark') +
  guides(fill = guide_legend(title = "Assessment type")) 

This bar graph above is showing that students overall perform much better in coursework than exams…but does ethnicity have an effect on this?? Will look at this later

Next i wanted to evaluate the average yearly mark dependent on assessment type. This shows every year the average grade is much bigger for cw type assessments vs exams. Essentially students perform much better in coursework type assessments in comparison to exams.

full_data %>%
  select(INSTANCE_ACADEMIC_YEAR, ASSESSMENT_COMPONENT_TYPE_DESC,MARK) %>%
  group_by(INSTANCE_ACADEMIC_YEAR, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  tally(mean(MARK)) %>%
  rename(Average_mark = n) %>%
  spread(key = ASSESSMENT_COMPONENT_TYPE_DESC, value = Average_mark) %>%
  mutate(Gap = Coursework - Exam)

This line graph below shows many things: 1. students perform much better in cw vs exams 2. Average cw mark is reducing over the years 3. Average exam mark is increasing over the years, but goes down in 2021 4. assessment awarding gap is reducing.

#Visualisation of above
full_data %>%
  select(INSTANCE_ACADEMIC_YEAR, ASSESSMENT_COMPONENT_TYPE_DESC,MARK) %>%
  group_by(INSTANCE_ACADEMIC_YEAR, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  tally(mean(MARK)) %>%
  rename(Average_mark = n) %>%
  #spread(key = ASSESSMENT_COMPONENT_TYPE_DESC, value = Average_mark) %>%
  #mutate(Gap = Coursework - Exam) %>%
  ggplot(aes(x = INSTANCE_ACADEMIC_YEAR, y = Average_mark, col = ASSESSMENT_COMPONENT_TYPE_DESC)) +
  geom_line() +
  geom_point() +
  ggtitle("Average yearly Mark for Coursework vs Exam (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Academic Year') +
  ylab('Average Mark') +
  labs(col='Assessment Type') 

Analysis of effect of assessment type on achieving top degree(2017-2022)

What about effect of assessment type on top degrees? This bar chart shows the average number of students that did cw vs exam in all students that achieved a top degree.

#average percentage of students who did cw vs exam to get first
full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(Average_mark >=60) %>%
  select(ID2, ASSESSMENT_COMPONENT_TYPE_DESC, Average_mark) %>%
  distinct() %>%
  group_by(ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=ASSESSMENT_COMPONENT_TYPE_DESC, y = counts, fill = ASSESSMENT_COMPONENT_TYPE_DESC)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Pastel2") +
  geom_text(aes(label = percentage), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Percentage of students doing cw vs exam for top degree") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Assessment Type') +
  ylab('Number of students') +
  guides(fill = guide_legend(title = "Assessment type")) 

This bar chart is showing yearly average percentage of students who did cw vs exam to get first This results are pretty consistent over the years. Has reduced slightly over the years. Gap in 21 looks pretty small, but number of students in this year was lower.

#percentage of students who did cw vs exam to get first (2017-2022)
  full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(Average_mark >=60) %>%
  select(ID2, ASSESSMENT_COMPONENT_TYPE_DESC, Average_mark, INSTANCE_ACADEMIC_YEAR) %>%
  distinct() %>%
  group_by(INSTANCE_ACADEMIC_YEAR, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=INSTANCE_ACADEMIC_YEAR, y = counts, fill =ASSESSMENT_COMPONENT_TYPE_DESC)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Pastel1") +
  geom_text(aes(label = percentage), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Percentage of students doing cw vs exam for top degree each year") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Academic Year') +
  ylab('Number of students') +
  guides(fill = guide_legend(title = "Assessment type")) 
`summarise()` has grouped output by 'INSTANCE_ACADEMIC_YEAR'. You can override using the `.groups` argument.

1.3 Analysing effect of assessment type on student performance and BAME awarding gap

#Checking there is a reasonable number of white/others in each assessment type. 
full_data %>%
  select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  group_by(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  tally()

First i want to look at affect of assessment type on student performance(White vs Other) This table below shows the difference in avgerage cw and exam marks for white Vs Bame students

Average grade over past 5 years for assessment type (AT) and ethnicity

full_data %>%
  select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC,MARK) %>%
  group_by(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  tally(mean(MARK))  %>%
  rename(Average_mark = n) %>%
   spread(key = Ethnicity, value = Average_mark) %>%
  mutate(gap = White - BAME)

The gap doesnt look huge, but it is bigger for coursework type assessments. However this is gap in average mark (student performance), not the number of students who achieved a top degree. Therefore, this is not relating to Bame awarding gap. Still interesting…

Visualisation of above table Looking at average grade over past 5 years for AT and ethnicity

full_data %>%
  select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC,MARK) %>%
  group_by(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  tally(mean(MARK))  %>%
  rename(Average_mark = n) %>%
   #spread(key = Ethnicity, value = Average_mark) %>%
  ggplot(aes(x=ASSESSMENT_COMPONENT_TYPE_DESC, y = Average_mark, fill = Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge')  + 
  scale_fill_brewer(palette = "Accent") +
  geom_text(aes(label = round(Average_mark, 2)), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Average Mark By Assessment Type And Ethnicity") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Assessment Type') 

  ylab('Average Mark')
$y
[1] "Average Mark"

attr(,"class")
[1] "labels"

This graph above is useful as it shows difference in average mark for each assessment type by ethnicity. However this research question pertains to the affect of assessment type on BAME awarding gaps. Therefore, instead of average marks, we need to compare the number of white vs BAME students who achieved top degrees and see if gap is greater for cw type assessments or coursework.

full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(Average_mark >=60) %>%
  select(ID2, Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC, Average_mark) %>%
  distinct() %>%
  select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  group_by(Ethnicity,ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  #select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC, percentage) %>%
  ggplot(aes(x=ASSESSMENT_COMPONENT_TYPE_DESC, y = percentage, fill = Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge')  + 
  scale_fill_brewer(palette = "Set1") +
  geom_text(aes(label = percentage), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Effect of assessment type on awarding gap") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Assessment Type') +
  ylab('Percentage of students')
Adding missing grouping variables: `ID2`
`summarise()` has grouped output by 'Ethnicity'. You can override using the `.groups` argument.

The results here are interesting. There is a 4% awarding gap for both ethnicities in each assessment type- this suggests exam do not lead to a wider awarding gap for the years 2017-2021.White students still performing better for both assessment types, but both ethnicities performing similar for assessment type.

#interaction plot of full dataset. 
interaction.plot(x.factor = full_data$Ethnicity, #x-axis variable
                 trace.factor = full_data$ASSESSMENT_COMPONENT_TYPE_DESC, #variable for lines
                 response = full_data$MARK, #y-axis variable
                 fun = median, #metric to plot
                 ylab = "Average Mark",
                 xlab = "Ethnicity",
                 col = c("red", "blue"),
                 lty = 1, #line type
                 lwd = 2, #line width
                 trace.label = "Assessment type")

Statistical Analysis/Modelling

Mixed model 1

#Question 1:
#   1.  What affect does the assessment type have on student performance?

#fitting model
mmodel1 <- lmer(MARK ~ ASSESSMENT_COMPONENT_TYPE_DESC * Ethnicity + PROGRAMME_PART + (1|ID2) + (1|MODULE_CODE), data = full_data)
summary(mmodel1)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: MARK ~ ASSESSMENT_COMPONENT_TYPE_DESC * Ethnicity + PROGRAMME_PART +  
    (1 | ID2) + (1 | MODULE_CODE)
   Data: full_data

REML criterion at convergence: 6688052

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-7.4683 -0.5331  0.0220  0.5787  4.7933 

Random effects:
 Groups      Name        Variance Std.Dev.
 ID2         (Intercept)  38.83    6.232  
 MODULE_CODE (Intercept)  25.44    5.043  
 Residual                153.51   12.390  
Number of obs: 840368, groups:  ID2, 31540; MODULE_CODE, 6945

Fixed effects:
                                                    Estimate Std. Error         df  t value
(Intercept)                                        6.537e+01  1.521e-01  1.530e+04  429.825
ASSESSMENT_COMPONENT_TYPE_DESCExam                -8.123e+00  5.911e-02  7.942e+05 -137.425
EthnicityWhite                                     3.333e+00  9.279e-02  3.443e+04   35.920
PROGRAMME_PARTB                                    5.245e-02  1.613e-01  1.550e+04    0.325
PROGRAMME_PARTC                                    7.652e-01  1.897e-01  1.295e+04    4.033
PROGRAMME_PARTD                                   -1.309e+00  3.776e-01  3.151e+04   -3.467
PROGRAMME_PARTF                                    5.816e+00  5.340e-01  6.400e+03   10.892
PROGRAMME_PARTT                                   -3.757e-01  1.903e-01  1.176e+04   -1.974
ASSESSMENT_COMPONENT_TYPE_DESCExam:EthnicityWhite  3.466e-01  6.729e-02  8.341e+05    5.151
                                                  Pr(>|t|)    
(Intercept)                                        < 2e-16 ***
ASSESSMENT_COMPONENT_TYPE_DESCExam                 < 2e-16 ***
EthnicityWhite                                     < 2e-16 ***
PROGRAMME_PARTB                                   0.744988    
PROGRAMME_PARTC                                   5.53e-05 ***
PROGRAMME_PARTD                                   0.000527 ***
PROGRAMME_PARTF                                    < 2e-16 ***
PROGRAMME_PARTT                                   0.048427 *  
ASSESSMENT_COMPONENT_TYPE_DESCExam:EthnicityWhite 2.59e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
                                 (Intr) ASSESSMENT_COMPONENT_TYPE_DESCEx EthncW
ASSESSMENT_COMPONENT_TYPE_DESCEx -0.130                                        
EthnictyWht                      -0.414  0.155                                 
PROGRAMME_PARTB                  -0.548 -0.001                            0.003
PROGRAMME_PARTC                  -0.525  0.000                            0.006
PROGRAMME_PARTD                  -0.281  0.004                            0.011
PROGRAMME_PARTF                  -0.216  0.003                            0.008
PROGRAMME_PARTT                  -0.716  0.037                            0.155
ASSESSMENT_COMPONENT_TYPE_DESCE:  0.083 -0.709                           -0.214
                                 PROGRAMME_PARTB PROGRAMME_PARTC PROGRAMME_PARTD
ASSESSMENT_COMPONENT_TYPE_DESCEx                                                
EthnictyWht                                                                     
PROGRAMME_PARTB                                                                 
PROGRAMME_PARTC                   0.510                                         
PROGRAMME_PARTD                   0.245           0.328                         
PROGRAMME_PARTF                   0.153           0.145           0.075         
PROGRAMME_PARTT                   0.440           0.427           0.255         
ASSESSMENT_COMPONENT_TYPE_DESCE: -0.003           0.000           0.000         
                                 PROGRAMME_PARTF PROGRAMME_PARTT
ASSESSMENT_COMPONENT_TYPE_DESCEx                                
EthnictyWht                                                     
PROGRAMME_PARTB                                                 
PROGRAMME_PARTC                                                 
PROGRAMME_PARTD                                                 
PROGRAMME_PARTF                                                 
PROGRAMME_PARTT                   0.170                         
ASSESSMENT_COMPONENT_TYPE_DESCE: -0.001          -0.010         
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.00454631 (tol = 0.002, component 1)
#interaction plot of model...

plot_model(mmodel1, type = "pred", terms = c("Ethnicity", "ASSESSMENT_COMPONENT_TYPE_DESC"))

anova(mmodel1)
Type III Analysis of Variance Table with Satterthwaite's method
                                          Sum Sq Mean Sq NumDF  DenDF   F value    Pr(>F)    
ASSESSMENT_COMPONENT_TYPE_DESC           5366486 5366486     1 717508 34959.266 < 2.2e-16 ***
Ethnicity                                 224560  224560     1  32669  1462.867 < 2.2e-16 ***
PROGRAMME_PART                             27274    5455     5  12989    35.534 < 2.2e-16 ***
ASSESSMENT_COMPONENT_TYPE_DESC:Ethnicity    4073    4073     1 834072    26.532 2.593e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#shows significance of all these variables on mark??

Results from the anova test show each of these variables have significant effect on mark

#effect size...look at video for how to explain this in report.
effectsize::eta_squared(mmodel1, partial = TRUE)
# Effect Size for ANOVA (Type III)

Parameter                                | Eta2 (partial) |       95% CI
------------------------------------------------------------------------
ASSESSMENT_COMPONENT_TYPE_DESC           |           0.05 | [0.05, 1.00]
Ethnicity                                |           0.04 | [0.04, 1.00]
PROGRAMME_PART                           |           0.01 | [0.01, 1.00]
ASSESSMENT_COMPONENT_TYPE_DESC:Ethnicity |       3.18e-05 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
#emmeans is meant to be looked at for main effect.
#in this case they were all significant so will do for each??

emmeans(mmodel1, list(pairwise~ASSESSMENT_COMPONENT_TYPE_DESC), adjust="tukey")
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 840368' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 840368' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
NOTE: Results may be misleading due to involvement in interactions
$`emmeans of ASSESSMENT_COMPONENT_TYPE_DESC`
 ASSESSMENT_COMPONENT_TYPE_DESC emmean     SE  df asymp.LCL asymp.UCL
 Coursework                      67.86 0.1263 Inf     67.62     68.11
 Exam                            59.91 0.1290 Inf     59.66     60.17

Results are averaged over the levels of: Ethnicity, PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95 

$`pairwise differences of ASSESSMENT_COMPONENT_TYPE_DESC`
 1                 estimate     SE  df z.ratio p.value
 Coursework - Exam     7.95 0.0425 Inf 186.974  <.0001

Results are averaged over the levels of: Ethnicity, PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
#here comparison shows that there is a sig diff in mark when assessment type is cw compared to exam (p.value = <0.0001)
VarCorr(mmodel1)
 Groups      Name        Std.Dev.
 ID2         (Intercept)  6.2315 
 MODULE_CODE (Intercept)  5.0433 
 Residual                12.3898 
totSD <- sqrt( 6.2315 + 5.0433 +  12.3898)
emmV <- emmeans(mmodel1, ~ ASSESSMENT_COMPONENT_TYPE_DESC)
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 840368' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 840368' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
NOTE: Results may be misleading due to involvement in interactions
eff_size(emmV, sigma = totSD, edf = 5)
 contrast          effect.size    SE  df asymp.LCL asymp.UCL
 Coursework - Exam        1.63 0.517 Inf     0.621      2.65

Results are averaged over the levels of: Ethnicity, PROGRAMME_PART 
sigma used for effect sizes: 4.865 
Degrees-of-freedom method: inherited from asymptotic when re-gridding 
Confidence level used: 0.95 
emmeans(mmodel1, list(pairwise~Ethnicity), adjust="tukey")
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 840368' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 840368' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
NOTE: Results may be misleading due to involvement in interactions
$`emmeans of Ethnicity`
 Ethnicity emmean     SE  df asymp.LCL asymp.UCL
 BAME       62.14 0.1373 Inf     61.87     62.41
 White      65.64 0.1305 Inf     65.39     65.90

Results are averaged over the levels of: ASSESSMENT_COMPONENT_TYPE_DESC, PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95 

$`pairwise differences of Ethnicity`
 1            estimate     SE  df z.ratio p.value
 BAME - White    -3.51 0.0917 Inf -38.247  <.0001

Results are averaged over the levels of: ASSESSMENT_COMPONENT_TYPE_DESC, PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
#same interpretation as above but for ethnicity??? but ask safa why is other-white not white-other.
emmV2 <- emmeans(mmodel1, ~ Ethnicity)
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 840368' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 840368' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
NOTE: Results may be misleading due to involvement in interactions
eff_size(emmV2, sigma = totSD, edf = 5)
 contrast     effect.size    SE  df asymp.LCL asymp.UCL
 BAME - White      -0.721 0.229 Inf     -1.17    -0.273

Results are averaged over the levels of: ASSESSMENT_COMPONENT_TYPE_DESC, PROGRAMME_PART 
sigma used for effect sizes: 4.865 
Degrees-of-freedom method: inherited from asymptotic when re-gridding 
Confidence level used: 0.95 
emmeans(mmodel1, list(pairwise~PROGRAMME_PART), adjust="tukey")
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 840368' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 840368' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
$`emmeans of PROGRAMME_PART`
 PROGRAMME_PART emmean     SE  df asymp.LCL asymp.UCL
 A               63.06 0.1387 Inf     62.79     63.34
 B               63.12 0.1356 Inf     62.85     63.38
 C               63.83 0.1582 Inf     63.52     64.14
 D               61.76 0.3606 Inf     61.05     62.46
 F               68.88 0.5194 Inf     67.86     69.90
 T               62.69 0.1308 Inf     62.43     62.95

Results are averaged over the levels of: ASSESSMENT_COMPONENT_TYPE_DESC, Ethnicity 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95 

$`pairwise differences of PROGRAMME_PART`
 1     estimate    SE  df z.ratio p.value
 A - B  -0.0525 0.161 Inf  -0.325  0.9995
 A - C  -0.7652 0.190 Inf  -4.033  0.0008
 A - D   1.3093 0.378 Inf   3.467  0.0070
 A - F  -5.8159 0.534 Inf -10.892  <.0001
 A - T   0.3757 0.190 Inf   1.974  0.3574
 B - C  -0.7127 0.175 Inf  -4.062  0.0007
 B - D   1.3617 0.372 Inf   3.656  0.0035
 B - F  -5.7634 0.534 Inf -10.800  <.0001
 B - T   0.4281 0.188 Inf   2.282  0.2014
 C - D   2.0744 0.363 Inf   5.717  <.0001
 C - F  -5.0507 0.540 Inf  -9.350  <.0001
 C - T   1.1408 0.203 Inf   5.609  <.0001
 D - F  -7.1252 0.630 Inf -11.304  <.0001
 D - T  -0.9336 0.377 Inf  -2.476  0.1312
 F - T   6.1916 0.536 Inf  11.561  <.0001

Results are averaged over the levels of: ASSESSMENT_COMPONENT_TYPE_DESC, Ethnicity 
Degrees-of-freedom method: asymptotic 
P value adjustment: tukey method for comparing a family of 6 estimates 
#interesting as only some parts show sig difference on mark... some parts e.g. B,T, show no sig diff on mark?? wonder why?? is this correct interpretation??
emmeans(mmodel1, pairwise ~ Ethnicity | ASSESSMENT_COMPONENT_TYPE_DESC)
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 840368' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 840368' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
$emmeans
ASSESSMENT_COMPONENT_TYPE_DESC = Coursework:
 Ethnicity emmean     SE  df asymp.LCL asymp.UCL
 BAME       66.20 0.1380 Inf     65.93     66.47
 White      69.53 0.1311 Inf     69.27     69.79

ASSESSMENT_COMPONENT_TYPE_DESC = Exam:
 Ethnicity emmean     SE  df asymp.LCL asymp.UCL
 BAME       58.07 0.1428 Inf     57.79     58.35
 White      61.75 0.1345 Inf     61.49     62.02

Results are averaged over the levels of: PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95 

$contrasts
ASSESSMENT_COMPONENT_TYPE_DESC = Coursework:
 contrast     estimate     SE  df z.ratio p.value
 BAME - White    -3.33 0.0928 Inf -35.920  <.0001

ASSESSMENT_COMPONENT_TYPE_DESC = Exam:
 contrast     estimate     SE  df z.ratio p.value
 BAME - White    -3.68 0.1023 Inf -35.974  <.0001

Results are averaged over the levels of: PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
emmV3 <- emmeans(mmodel1, ~ Ethnicity | ASSESSMENT_COMPONENT_TYPE_DESC)
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 840368' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 840368' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
eff_size(emmV3, sigma = totSD, edf = 5)
ASSESSMENT_COMPONENT_TYPE_DESC = Coursework:
 contrast     effect.size    SE  df asymp.LCL asymp.UCL
 BAME - White      -0.685 0.218 Inf     -1.11    -0.259

ASSESSMENT_COMPONENT_TYPE_DESC = Exam:
 contrast     effect.size    SE  df asymp.LCL asymp.UCL
 BAME - White      -0.756 0.240 Inf     -1.23    -0.286

Results are averaged over the levels of: PROGRAMME_PART 
sigma used for effect sizes: 4.865 
Degrees-of-freedom method: inherited from asymptotic when re-gridding 
Confidence level used: 0.95 

Evaluating mixed model-1 assumptions

linearity

Usually, there are 3 model assumptions which we need to check; linearity, homoscedasticity and normality of residuals. We do not need to look at linearity as all variables in this model are categorical.

Homoscedasticity:

#code adapted from https://ademos.people.uic.edu/Chapter18.html
plot(mmodel1)

boxplot(mmodel1@resp$wtres~full_data$ASSESSMENT_COMPONENT_TYPE_DESC)

boxplot(mmodel1@resp$wtres~full_data$Ethnicity)

boxplot(mmodel1@resp$wtres~full_data$PROGRAMME_PART)

Another way to check for homoscedasticity - (sd) residuals

#ASSESSMENT TYPE
mmodel1res<-mmodel1@resp$wtres
#assessment type residuals in df
mmodel1df<-data.frame(full_data$ASSESSMENT_COMPONENT_TYPE_DESC,mmodel1res) 
names(mmodel1df)<-c("ASSESSMENT_COMPONENT_TYPE_DESC","residual")
mmodel1df %>% group_by(ASSESSMENT_COMPONENT_TYPE_DESC)%>% summarise(sd(residual)) 

Since the highest sd is less than twice the lowest, that is fine (according to general rule of thumb for homoscedasticity across groups of a factor)

Now we will check for the remaining variables…

#Ethnicity
mmodel1df2<-data.frame(full_data$Ethnicity,mmodel1res) 
names(mmodel1df2)<-c("Ethnicity","residual")
mmodel1df2 %>% group_by(Ethnicity)%>% summarise(sd(residual)) 

#assumption satisfied 
#programme part
mmodel1df3<-data.frame(full_data$PROGRAMME_PART,mmodel1res) 
names(mmodel1df3)<-c("Programme_Part","residual")
mmodel1df3 %>% group_by(Programme_Part)%>% summarise(sd(residual))

#assumption met for this categorical variable as the largest residual is 15.3 and is less than 2* the smallest residual of D (9.20)

normality of residuals

qqnorm(mmodel1res)

Confidence intervals

#Interval estimates for mixed models
#confint(mmodel1)

The above model is assessing effect of variables on student performance by looking at average marks. We want to look at awarding gaps so will categorise the marks into top degree and below and run a binomial model.

Generalised linear mixed model 1.2

#creating binomial response variable
full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  mutate(Grade = case_when(Average_mark >= 60 ~ 1,
                           Average_mark < 60 ~ 0)) -> full_data3
#1. What affect does the assessment type have on the BAME awarding gap?
linmod<-glmer(as.factor(Grade) ~ ASSESSMENT_COMPONENT_TYPE_DESC * Ethnicity + PROGRAMME_PART + (1|ID2) + (1|MODULE_CODE), family="binomial",data = full_data3, nAGQ=0)
summary(linmod)
Generalized linear mixed model fit by maximum likelihood (Adaptive Gauss-Hermite Quadrature,
  nAGQ = 0) [glmerMod]
 Family: binomial  ( logit )
Formula: as.factor(Grade) ~ ASSESSMENT_COMPONENT_TYPE_DESC * Ethnicity +  
    PROGRAMME_PART + (1 | ID2) + (1 | MODULE_CODE)
   Data: full_data3

     AIC      BIC   logLik deviance df.resid 
 52332.8  52460.9 -26155.4  52310.8   840357 

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-0.18149  0.01053  0.01402  0.01861  0.07701 

Random effects:
 Groups      Name        Variance  Std.Dev. 
 ID2         (Intercept) 3.070e+02 1.752e+01
 MODULE_CODE (Intercept) 6.814e-12 2.610e-06
Number of obs: 840368, groups:  ID2, 31540; MODULE_CODE, 6945

Fixed effects:
                                                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)                                         3.5760     0.2312  15.466  < 2e-16 ***
ASSESSMENT_COMPONENT_TYPE_DESCExam                 -0.1197     0.1662  -0.720  0.47152    
EthnicityWhite                                      2.3745     0.2582   9.196  < 2e-16 ***
PROGRAMME_PARTB                                     0.4248     0.1625   2.614  0.00895 ** 
PROGRAMME_PARTC                                     0.5394     0.2511   2.148  0.03170 *  
PROGRAMME_PARTD                                     1.9214     1.8806   1.022  0.30694    
PROGRAMME_PARTF                                    -0.5267     0.5435  -0.969  0.33257    
PROGRAMME_PARTT                                    -0.1385     0.2593  -0.534  0.59341    
ASSESSMENT_COMPONENT_TYPE_DESCExam:EthnicityWhite  -0.0483     0.2357  -0.205  0.83768    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
                                 (Intr) ASSESSMENT_COMPONENT_TYPE_DESCEx EthncW
ASSESSMENT_COMPONENT_TYPE_DESCEx -0.237                                        
EthnictyWht                      -0.728  0.196                                 
PROGRAMME_PARTB                  -0.185 -0.019                            0.006
PROGRAMME_PARTC                  -0.134 -0.009                            0.007
PROGRAMME_PARTD                  -0.020  0.003                            0.002
PROGRAMME_PARTF                  -0.131  0.008                            0.030
PROGRAMME_PARTT                  -0.687  0.095                            0.392
ASSESSMENT_COMPONENT_TYPE_DESCE:  0.144 -0.701                           -0.272
                                 PROGRAMME_PARTB PROGRAMME_PARTC PROGRAMME_PARTD
ASSESSMENT_COMPONENT_TYPE_DESCEx                                                
EthnictyWht                                                                     
PROGRAMME_PARTB                                                                 
PROGRAMME_PARTC                   0.282                                         
PROGRAMME_PARTD                   0.037           0.038                         
PROGRAMME_PARTF                   0.080           0.052           0.008         
PROGRAMME_PARTT                   0.168           0.122           0.017         
ASSESSMENT_COMPONENT_TYPE_DESCE: -0.001           0.000          -0.002         
                                 PROGRAMME_PARTF PROGRAMME_PARTT
ASSESSMENT_COMPONENT_TYPE_DESCEx                                
EthnictyWht                                                     
PROGRAMME_PARTB                                                 
PROGRAMME_PARTC                                                 
PROGRAMME_PARTD                                                 
PROGRAMME_PARTF                                                 
PROGRAMME_PARTT                   0.107                         
ASSESSMENT_COMPONENT_TYPE_DESCE:  0.001          -0.030         
optimizer (bobyqa) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
se <- sqrt(diag(vcov(linmod)))
# table of estimates with 95% CI
(tab <- cbind(Est = fixef(linmod), LL = fixef(linmod) - 1.96 * se, UL = fixef(linmod) + 1.96 *
    se))
                                                          Est          LL        UL
(Intercept)                                        3.57595111  3.12276002 4.0291422
ASSESSMENT_COMPONENT_TYPE_DESCExam                -0.11965227 -0.44536946 0.2060649
EthnicityWhite                                     2.37450940  1.86844174 2.8805771
PROGRAMME_PARTB                                    0.42478261  0.10625209 0.7433131
PROGRAMME_PARTC                                    0.53936330  0.04726069 1.0314659
PROGRAMME_PARTD                                    1.92137632 -1.76467657 5.6074292
PROGRAMME_PARTF                                   -0.52666637 -1.59200991 0.5386772
PROGRAMME_PARTT                                   -0.13846290 -0.64677264 0.3698468
ASSESSMENT_COMPONENT_TYPE_DESCExam:EthnicityWhite -0.04829525 -0.51035739 0.4137669
exp(tab)
                                                         Est         LL         UL
(Intercept)                                       35.7285865 22.7089705  56.212671
ASSESSMENT_COMPONENT_TYPE_DESCExam                 0.8872289  0.6405876   1.228833
EthnicityWhite                                    10.7457400  6.4781938  17.824556
PROGRAMME_PARTB                                    1.5292579  1.1121022   2.102891
PROGRAMME_PARTC                                    1.7149146  1.0483953   2.805175
PROGRAMME_PARTD                                    6.8303527  0.1712422 272.442940
PROGRAMME_PARTF                                    0.5905704  0.2035162   1.713738
PROGRAMME_PARTT                                    0.8706956  0.5237333   1.447513
ASSESSMENT_COMPONENT_TYPE_DESCExam:EthnicityWhite  0.9528524  0.6002810   1.512504
#evaluating model significance/quality 
#Analysis of Deviance 
#install.packages("car")
#library("car")
Anova(linmod)
Analysis of Deviance Table (Type II Wald chisquare tests)

Response: as.factor(Grade)
                                           Chisq Df Pr(>Chisq)    
ASSESSMENT_COMPONENT_TYPE_DESC            1.4648  1    0.22616    
Ethnicity                                90.2155  1    < 2e-16 ***
PROGRAMME_PART                           12.3015  5    0.03088 *  
ASSESSMENT_COMPONENT_TYPE_DESC:Ethnicity  0.0420  1    0.83768    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
plot_model(linmod, type = "pred", terms = c("Ethnicity", "ASSESSMENT_COMPONENT_TYPE_DESC"))

emmeans(linmod, ~ ASSESSMENT_COMPONENT_TYPE_DESC, type = "response")
NOTE: Results may be misleading due to involvement in interactions
 ASSESSMENT_COMPONENT_TYPE_DESC response       SE  df asymp.LCL asymp.UCL
 Coursework                       0.9941 0.002031 Inf    0.9885    0.9970
 Exam                             0.9932 0.002389 Inf    0.9865    0.9966

Results are averaged over the levels of: Ethnicity, PROGRAMME_PART 
Unknown transformation "as.factor": no transformation done 
Confidence level used: 0.95 
emmeans(linmod, ~ Ethnicity, type = "response")
NOTE: Results may be misleading due to involvement in interactions
 Ethnicity response        SE  df asymp.LCL asymp.UCL
 BAME        0.9799 0.0074121 Inf    0.9589    0.9903
 White       0.9980 0.0007064 Inf    0.9960    0.9990

Results are averaged over the levels of: ASSESSMENT_COMPONENT_TYPE_DESC, PROGRAMME_PART 
Unknown transformation "as.factor": no transformation done 
Confidence level used: 0.95 
emmeans(linmod, ~ Ethnicity | ASSESSMENT_COMPONENT_TYPE_DESC, type = "response")
ASSESSMENT_COMPONENT_TYPE_DESC = Coursework:
 Ethnicity response        SE  df asymp.LCL asymp.UCL
 BAME        0.9810 0.0070326 Inf    0.9610    0.9909
 White       0.9982 0.0006544 Inf    0.9963    0.9991

ASSESSMENT_COMPONENT_TYPE_DESC = Exam:
 Ethnicity response        SE  df asymp.LCL asymp.UCL
 BAME        0.9787 0.0081866 Inf    0.9551    0.9900
 White       0.9979 0.0008030 Inf    0.9955    0.9990

Results are averaged over the levels of: PROGRAMME_PART 
Unknown transformation "as.factor": no transformation done 
Confidence level used: 0.95 

This model does not require checking of assumptions

Research Question 2

2. What affect does the assessment type have on the performance of Non-White students (Namely Asian, Black, Chinese, and mixed) and the awarding gap?

Data wrangling: This research question required different categorisisation of ethnicities.

#categorising ethnicities into Asian, Black, Chinese, Mixed & White

clean_ethnicity_data2[clean_ethnicity_data2 == "White - British"] <- "White"
clean_ethnicity_data2[clean_ethnicity_data2 == "White - Irish"] <- "White"
clean_ethnicity_data2[clean_ethnicity_data2 == "White - Scottish"] <- "White"
clean_ethnicity_data2[clean_ethnicity_data2 == "Other White Background"] <- "White"

clean_ethnicity_data2[clean_ethnicity_data2 == "Asian Or Asian British - Bangladeshi"] <- "Asian"
clean_ethnicity_data2[clean_ethnicity_data2 == "Asian Or Asian British - Indian"] <- "Asian"
clean_ethnicity_data2[clean_ethnicity_data2 == "Asian Or Asian British - Pakistani"] <- "Asian"
clean_ethnicity_data2[clean_ethnicity_data2 == "Black Or Black British - African"] <- "Black"
clean_ethnicity_data2[clean_ethnicity_data2 == "Black Or Black British - Caribbean"] <- "Black"
clean_ethnicity_data2[clean_ethnicity_data2 == "Chinese"] <- "Chinese"
clean_ethnicity_data2[clean_ethnicity_data2 == "Mixed - White And Asian"] <- "Mixed"
clean_ethnicity_data2[clean_ethnicity_data2 == "Mixed - White And Black African"] <- "Mixed"
clean_ethnicity_data2[clean_ethnicity_data2 == "Mixed - White And Black Caribbean"] <- "Mixed"
clean_ethnicity_data2[clean_ethnicity_data2 == "Other Asian Background"] <- "Asian"
clean_ethnicity_data2[clean_ethnicity_data2 == "Other Black Background"] <- "Black"
clean_ethnicity_data2[clean_ethnicity_data2 == "Other Mixed Background"] <- "Mixed"

#remove not known,no information & info refused- as could be any ethnicity.
#also removed gypsy as only 1 student- not good for representation
#also removed other ethnic background- as not sure what category this relates to 

clean_ethnicity_data2<-clean_ethnicity_data2[!(clean_ethnicity_data2$Ethnicity=="Information Refused" | clean_ethnicity_data2$Ethnicity=="Information Refused - OBSOLETE VALUE" | clean_ethnicity_data2$Ethnicity=="No Information" | clean_ethnicity_data2$Ethnicity=="Not Known" | clean_ethnicity_data2$Ethnicity=="Gypsy or Traveller" | clean_ethnicity_data2$Ethnicity=="Other Ethnic Background" | clean_ethnicity_data2$Ethnicity=="Arab") ,]
clean_ethnicity_data2 %>%
  mutate_at(vars(Gender, Ethnicity), list(factor)) -> clean_ethnicity_data2

summary(clean_ethnicity_data2)
      ID2          Gender        Ethnicity    
 Min.   :    1   Man  :24318   Asian  : 5408  
 1st Qu.:11056   Woman:17820   Black  : 2814  
 Median :22126                 Chinese: 6688  
 Mean   :22104                 Mixed  : 1865  
 3rd Qu.:33135                 White  :25363  
 Max.   :44191                                
#Ethnicity
#Percentage of students belonging to each ethnicity after categorisation
clean_ethnicity_data2 %>%
  group_by(Ethnicity) %>%
  summarise(counts = n()) %>%
  mutate(freq = (counts / sum(counts))) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot( ., aes(x="", y=freq, fill=Ethnicity)) +
  geom_bar(stat="identity", width=1, color="white") +
  coord_polar("y", start=0) +
  theme_void() +
  geom_text(aes(label = percentage), color = "white", position = position_stack(vjust = 0.5), size = 2.5) +
  scale_fill_brewer(palette = "Paired") +
  ggtitle("Percentage of students in each ethnic category") +
  theme(plot.title = element_text(hjust = 0.5)) 

Joining two datasets

#JOIN ETHNIC DATA AND MARK DATA
inner_join(clean_ethnicity_data2, clean_mark_data) -> full_data2
print(distinct(full_data2))

2.1 Effect of Ethncicity (Non-White students) on student performance

boxplot(MARK~Ethnicity, data = full_data2)

#Visualise effect of ethnicity on overall/mean mark between 2017-2021
full_data2 %>%
  select(Ethnicity, MARK) %>%
  group_by(Ethnicity) %>%
  tally(mean(MARK))  %>%
  rename(Average_mark = n) %>%
  ggplot(aes(x=Ethnicity, y = Average_mark, fill =Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Paired") +
  geom_text(aes(label = round(Average_mark, 2)), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Average Test Scores by Ethnicity between 2017-2021") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ylab('Average Mark') 

#looking at avg grade for each ethnicity type between 2017-2021
full_data2 %>%
  select(INSTANCE_ACADEMIC_YEAR, Ethnicity,MARK) %>%
  group_by(INSTANCE_ACADEMIC_YEAR, Ethnicity) %>%
  tally(mean(MARK)) %>%
  rename(Average_mark = n) %>%
  #spread(key = Ethnicity, value = Average_mark) %>%
  ggplot(aes(x = INSTANCE_ACADEMIC_YEAR, y = Average_mark, col = Ethnicity)) +
  geom_line() +
  geom_point() +
  ggtitle("Average grade for White vs BAME (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Academic Year') +
  ylab('Average Mark') +
  labs(col='Ethnicity') 

Analysis of Non-white students Awarding Gap (2017-2021)

#Visualising percentage of white vs BAME students achieving top degree (2017-2021)
full_data2 %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(Average_mark >=60) %>%
  select(ID2, Ethnicity, Average_mark) %>%
  distinct() %>%
  group_by(Ethnicity) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=Ethnicity, y = counts, fill =Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Pastel1") +
  geom_text(aes(label = percentage), position = position_stack(vjust = 1.1), size = 3)  +
  ggtitle("Percentage of white vs BAME students achieving top degree (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ylab('Number of students')

#BAME AWARDING GAP
full_data2 %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  mutate(Grade = case_when(Average_mark >= 70 ~ "First Class",
                           Average_mark >= 60 ~ "Upper Second Class Honour",
                           Average_mark >= 50 ~ "Lower Second Class Honour",
                           Average_mark >= 40 ~ "Third class/pass",
                           Average_mark < 40 ~ "Fail")) %>%
  group_by(Ethnicity, Grade) %>%
  summarise(counts = n()) %>%
  mutate(freq = (counts / sum(counts))) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=Ethnicity, y = freq, fill =Grade)) +
  geom_bar(stat = 'identity') + 
  coord_flip() +
  scale_fill_brewer(palette = "Pastel1") +
  geom_text(aes(label = percentage), position = position_stack(vjust = 0.5), size = 2.5) +
  ggtitle("Distribution of degree outcomes by ethnic group (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  labs(y = "Frequency", fill = "Degree Classification")
`summarise()` has grouped output by 'Ethnicity'. You can override using the `.groups` argument.

2.2 Analysing effect of assessment type on non white students performance and awarding gap

full_data2 %>%
  select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC,MARK) %>%
  group_by(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  tally(mean(MARK))  %>%
  rename(Average_mark = n) %>%
   #spread(key = Ethnicity, value = Average_mark) %>%
  ggplot(aes(x=ASSESSMENT_COMPONENT_TYPE_DESC, y = Average_mark, fill = Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge')  + 
  scale_fill_brewer(palette = "Set1") +
  geom_text(aes(label = round(Average_mark, 2)), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Average Mark By Assessment Type And Ethnicity") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Assessment Type') +
  ylab('Average Mark')

Effect of assessment type on awarding gap for Non-white categorise.

full_data2 %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(Average_mark >=60) %>%
  select(ID2, Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC, Average_mark) %>%
  distinct() %>%
  select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  group_by(Ethnicity,ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  #select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC, percentage) %>%
  ggplot(aes(x=ASSESSMENT_COMPONENT_TYPE_DESC, y = percentage, fill = Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge')  + 
  scale_fill_brewer(palette = "Set1") +
  geom_text(aes(label = percentage), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Effect of assessment type on awarding gap") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Assessment Type') +
  ylab('Percentage of students')
Adding missing grouping variables: `ID2`
`summarise()` has grouped output by 'Ethnicity'. You can override using the `.groups` argument.

interaction.plot(x.factor = full_data2$Ethnicity, #x-axis variable
                 trace.factor = full_data2$ASSESSMENT_COMPONENT_TYPE_DESC, #variable for lines
                 response = full_data2$MARK, #y-axis variable
                 fun = median, #metric to plot
                 ylab = "Mark",
                 xlab = "Ethnicity",
                 col = c("pink", "lightblue"),
                 lty = 1, #line type
                 lwd = 2, #line width
                 trace.label = "Assessment type")

Statistical Analysis

Mixed Model 2

#Question 2:
#   2.  What affect does the assessment type have on the performance of Non-White students (Namely Asian, Black, Chinese, and mixed)

mmodel2<- lmer(MARK ~ ASSESSMENT_COMPONENT_TYPE_DESC * Ethnicity + PROGRAMME_PART + (1|ID2) + (1|MODULE_CODE), data = full_data2)
summary(mmodel2)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: MARK ~ ASSESSMENT_COMPONENT_TYPE_DESC * Ethnicity + PROGRAMME_PART +  
    (1 | ID2) + (1 | MODULE_CODE)
   Data: full_data2

REML criterion at convergence: 6585014

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-7.4776 -0.5326  0.0220  0.5783  4.8132 

Random effects:
 Groups      Name        Variance Std.Dev.
 ID2         (Intercept)  37.93    6.158  
 MODULE_CODE (Intercept)  25.28    5.028  
 Residual                153.15   12.375  
Number of obs: 827711, groups:  ID2, 31062; MODULE_CODE, 6939

Fixed effects:
                                                      Estimate Std. Error         df t value
(Intercept)                                          6.634e+01  1.747e-01  2.325e+04 379.791
ASSESSMENT_COMPONENT_TYPE_DESCExam                  -8.116e+00  8.779e-02  8.194e+05 -92.448
EthnicityBlack                                      -1.485e+00  1.895e-01  3.176e+04  -7.835
EthnicityChinese                                    -3.433e+00  1.631e-01  3.652e+04 -21.052
EthnicityMixed                                       9.747e-01  2.231e-01  3.176e+04   4.369
EthnicityWhite                                       2.288e+00  1.269e-01  3.332e+04  18.035
PROGRAMME_PARTB                                      4.924e-02  1.615e-01  1.522e+04   0.305
PROGRAMME_PARTC                                      7.788e-01  1.898e-01  1.277e+04   4.102
PROGRAMME_PARTD                                     -1.202e+00  3.782e-01  3.099e+04  -3.179
PROGRAMME_PARTF                                      5.688e+00  5.341e-01  6.438e+03  10.649
PROGRAMME_PARTT                                      3.115e-01  1.935e-01  1.250e+04   1.609
ASSESSMENT_COMPONENT_TYPE_DESCExam:EthnicityBlack   -1.281e+00  1.393e-01  8.183e+05  -9.197
ASSESSMENT_COMPONENT_TYPE_DESCExam:EthnicityChinese  1.423e+00  1.449e-01  8.248e+05   9.823
ASSESSMENT_COMPONENT_TYPE_DESCExam:EthnicityMixed   -1.760e-01  1.620e-01  8.175e+05  -1.086
ASSESSMENT_COMPONENT_TYPE_DESCExam:EthnicityWhite    3.375e-01  9.320e-02  8.203e+05   3.622
                                                    Pr(>|t|)    
(Intercept)                                          < 2e-16 ***
ASSESSMENT_COMPONENT_TYPE_DESCExam                   < 2e-16 ***
EthnicityBlack                                      4.84e-15 ***
EthnicityChinese                                     < 2e-16 ***
EthnicityMixed                                      1.25e-05 ***
EthnicityWhite                                       < 2e-16 ***
PROGRAMME_PARTB                                     0.760414    
PROGRAMME_PARTC                                     4.12e-05 ***
PROGRAMME_PARTD                                     0.001478 ** 
PROGRAMME_PARTF                                      < 2e-16 ***
PROGRAMME_PARTT                                     0.107560    
ASSESSMENT_COMPONENT_TYPE_DESCExam:EthnicityBlack    < 2e-16 ***
ASSESSMENT_COMPONENT_TYPE_DESCExam:EthnicityChinese  < 2e-16 ***
ASSESSMENT_COMPONENT_TYPE_DESCExam:EthnicityMixed   0.277308    
ASSESSMENT_COMPONENT_TYPE_DESCExam:EthnicityWhite   0.000293 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation matrix not shown by default, as p = 15 > 12.
Use print(x, correlation=TRUE)  or
    vcov(x)        if you need it
#same as above- but shows more values ...
plot_model(mmodel2, type = "pred", terms = c("Ethnicity", "ASSESSMENT_COMPONENT_TYPE_DESC"))

anova(mmodel2)
Type III Analysis of Variance Table with Satterthwaite's method
                                          Sum Sq Mean Sq NumDF  DenDF   F value    Pr(>F)    
ASSESSMENT_COMPONENT_TYPE_DESC           3439838 3439838     1 764529 22460.340 < 2.2e-16 ***
Ethnicity                                 277515   69379     4  32481   453.007 < 2.2e-16 ***
PROGRAMME_PART                             23368    4674     5  13145    30.516 < 2.2e-16 ***
ASSESSMENT_COMPONENT_TYPE_DESC:Ethnicity   46918   11730     4 822301    76.588 < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Similar to RQ1, all variables hold a significant effect on student performance

#effect size...look at video for how to explain this in report.
effectsize::eta_squared(mmodel2, partial = TRUE)
# Effect Size for ANOVA (Type III)

Parameter                                | Eta2 (partial) |       95% CI
------------------------------------------------------------------------
ASSESSMENT_COMPONENT_TYPE_DESC           |           0.03 | [0.03, 1.00]
Ethnicity                                |           0.05 | [0.05, 1.00]
PROGRAMME_PART                           |           0.01 | [0.01, 1.00]
ASSESSMENT_COMPONENT_TYPE_DESC:Ethnicity |       3.72e-04 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
#emmeans is meant to be looked at for main effect.
#in this case they were all significant so will do for each??

emmeans(mmodel2, list(pairwise~ASSESSMENT_COMPONENT_TYPE_DESC), adjust="tukey")
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 827711' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 827711)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 827711' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 827711)' or larger];
but be warned that this may result in large computation time and memory use.
NOTE: Results may be misleading due to involvement in interactions
$`emmeans of ASSESSMENT_COMPONENT_TYPE_DESC`
 ASSESSMENT_COMPONENT_TYPE_DESC emmean     SE  df asymp.LCL asymp.UCL
 Coursework                      66.94 0.1335 Inf     66.68     67.20
 Exam                            58.89 0.1376 Inf     58.62     59.16

Results are averaged over the levels of: Ethnicity, PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95 

$`pairwise differences of ASSESSMENT_COMPONENT_TYPE_DESC`
 1                 estimate     SE  df z.ratio p.value
 Coursework - Exam     8.06 0.0538 Inf 149.868  <.0001

Results are averaged over the levels of: Ethnicity, PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
emmeans(mmodel2, list(pairwise~Ethnicity), adjust="tukey")
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 827711' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 827711)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 827711' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 827711)' or larger];
but be warned that this may result in large computation time and memory use.
NOTE: Results may be misleading due to involvement in interactions
$`emmeans of Ethnicity`
 Ethnicity emmean     SE  df asymp.LCL asymp.UCL
 Asian      63.22 0.1618 Inf     62.90     63.53
 Black      61.09 0.1919 Inf     60.72     61.47
 Chinese    60.49 0.1771 Inf     60.15     60.84
 Mixed      64.10 0.2227 Inf     63.67     64.54
 White      65.67 0.1303 Inf     65.42     65.93

Results are averaged over the levels of: ASSESSMENT_COMPONENT_TYPE_DESC, PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95 

$`pairwise differences of Ethnicity`
 1               estimate    SE  df z.ratio p.value
 Asian - Black      2.125 0.187 Inf  11.391  <.0001
 Asian - Chinese    2.722 0.165 Inf  16.481  <.0001
 Asian - Mixed     -0.887 0.219 Inf  -4.042  0.0005
 Asian - White     -2.457 0.125 Inf -19.692  <.0001
 Black - Chinese    0.597 0.197 Inf   3.029  0.0207
 Black - Mixed     -3.012 0.243 Inf -12.409  <.0001
 Black - White     -4.582 0.163 Inf -28.163  <.0001
 Chinese - Mixed   -3.608 0.231 Inf -15.608  <.0001
 Chinese - White   -5.179 0.145 Inf -35.600  <.0001
 Mixed - White     -1.570 0.197 Inf  -7.985  <.0001

Results are averaged over the levels of: ASSESSMENT_COMPONENT_TYPE_DESC, PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
P value adjustment: tukey method for comparing a family of 5 estimates 
emmeans(mmodel2, list(pairwise~PROGRAMME_PART), adjust="tukey")
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 827711' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 827711)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 827711' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 827711)' or larger];
but be warned that this may result in large computation time and memory use.
$`emmeans of PROGRAMME_PART`
 PROGRAMME_PART emmean     SE  df asymp.LCL asymp.UCL
 A               61.98 0.1461 Inf     61.69     62.26
 B               62.03 0.1431 Inf     61.75     62.31
 C               62.76 0.1646 Inf     62.43     63.08
 D               60.78 0.3638 Inf     60.06     61.49
 F               67.67 0.5214 Inf     66.64     68.69
 T               62.29 0.1372 Inf     62.02     62.56

Results are averaged over the levels of: ASSESSMENT_COMPONENT_TYPE_DESC, Ethnicity 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95 

$`pairwise differences of PROGRAMME_PART`
 1     estimate    SE  df z.ratio p.value
 A - B  -0.0492 0.161 Inf  -0.305  0.9996
 A - C  -0.7788 0.190 Inf  -4.102  0.0006
 A - D   1.2023 0.378 Inf   3.179  0.0185
 A - F  -5.6881 0.534 Inf -10.649  <.0001
 A - T  -0.3115 0.194 Inf  -1.609  0.5923
 B - C  -0.7296 0.176 Inf  -4.151  0.0005
 B - D   1.2515 0.373 Inf   3.355  0.0103
 B - F  -5.6388 0.534 Inf -10.563  <.0001
 B - T  -0.2623 0.191 Inf  -1.374  0.7426
 C - D   1.9811 0.363 Inf   5.450  <.0001
 C - F  -4.9093 0.540 Inf  -9.085  <.0001
 C - T   0.4673 0.206 Inf   2.266  0.2083
 D - F  -6.8904 0.631 Inf -10.923  <.0001
 D - T  -1.5138 0.379 Inf  -3.993  0.0009
 F - T   5.3766 0.537 Inf  10.012  <.0001

Results are averaged over the levels of: ASSESSMENT_COMPONENT_TYPE_DESC, Ethnicity 
Degrees-of-freedom method: asymptotic 
P value adjustment: tukey method for comparing a family of 6 estimates 
#There was an interaction so need emmeans for this
emmeans(mmodel2, pairwise ~ Ethnicity | ASSESSMENT_COMPONENT_TYPE_DESC)
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 827711' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 827711)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 827711' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 827711)' or larger];
but be warned that this may result in large computation time and memory use.
$emmeans
ASSESSMENT_COMPONENT_TYPE_DESC = Coursework:
 Ethnicity emmean     SE  df asymp.LCL asymp.UCL
 Asian      67.27 0.1633 Inf     66.95     67.59
 Black      65.79 0.1939 Inf     65.41     66.17
 Chinese    63.84 0.1750 Inf     63.50     64.18
 Mixed      68.25 0.2255 Inf     67.81     68.69
 White      69.56 0.1309 Inf     69.31     69.82

ASSESSMENT_COMPONENT_TYPE_DESC = Exam:
 Ethnicity emmean     SE  df asymp.LCL asymp.UCL
 Asian      59.16 0.1719 Inf     58.82     59.49
 Black      56.39 0.2066 Inf     55.99     56.80
 Chinese    57.15 0.1986 Inf     56.76     57.54
 Mixed      59.96 0.2416 Inf     59.48     60.43
 White      61.78 0.1343 Inf     61.52     62.05

Results are averaged over the levels of: PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95 

$contrasts
ASSESSMENT_COMPONENT_TYPE_DESC = Coursework:
 contrast        estimate    SE  df z.ratio p.value
 Asian - Black      1.485 0.190 Inf   7.835  <.0001
 Asian - Chinese    3.433 0.163 Inf  21.052  <.0001
 Asian - Mixed     -0.975 0.223 Inf  -4.369  0.0001
 Asian - White     -2.288 0.127 Inf -18.035  <.0001
 Black - Chinese    1.948 0.196 Inf   9.938  <.0001
 Black - Mixed     -2.459 0.247 Inf  -9.971  <.0001
 Black - White     -3.773 0.165 Inf -22.835  <.0001
 Chinese - Mixed   -4.408 0.232 Inf -19.025  <.0001
 Chinese - White   -5.721 0.143 Inf -40.129  <.0001
 Mixed - White     -1.313 0.200 Inf  -6.571  <.0001

ASSESSMENT_COMPONENT_TYPE_DESC = Exam:
 contrast        estimate    SE  df z.ratio p.value
 Asian - Black      2.766 0.208 Inf  13.276  <.0001
 Asian - Chinese    2.010 0.196 Inf  10.254  <.0001
 Asian - Mixed     -0.799 0.244 Inf  -3.272  0.0094
 Asian - White     -2.626 0.139 Inf -18.861  <.0001
 Black - Chinese   -0.755 0.229 Inf  -3.296  0.0087
 Black - Mixed     -3.564 0.270 Inf -13.203  <.0001
 Black - White     -5.391 0.181 Inf -29.788  <.0001
 Chinese - Mixed   -2.809 0.265 Inf -10.614  <.0001
 Chinese - White   -4.636 0.173 Inf -26.750  <.0001
 Mixed - White     -1.827 0.219 Inf  -8.355  <.0001

Results are averaged over the levels of: PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
P value adjustment: tukey method for comparing a family of 5 estimates 

Evaluating mixed model 2 assumptions

Homodscedasticity

#plot of mixed model 2
plot(mmodel2)

boxplot(mmodel2@resp$wtres~full_data2$ASSESSMENT_COMPONENT_TYPE_DESC)

boxplot(mmodel2@resp$wtres~full_data2$Ethnicity)

boxplot(mmodel2@resp$wtres~full_data2$PROGRAMME_PART)

Another way to check for homeoscedasticity - (sd) residuals

#ASSESSMENT TYPE
mmodel2res<-mmodel2@resp$wtres
#assessment type residuals in df
mmodel2df<-data.frame(full_data2$ASSESSMENT_COMPONENT_TYPE_DESC,mmodel2res) 
names(mmodel2df)<-c("ASSESSMENT_COMPONENT_TYPE_DESC","residual")
mmodel2df %>% group_by(ASSESSMENT_COMPONENT_TYPE_DESC)%>% summarise(sd(residual)) 

The assumption is met because highest sd is less than twice the lowest.

#Ethnicity
mmodel2df2<-data.frame(full_data2$Ethnicity,mmodel2res) 
names(mmodel2df2)<-c("Ethnicity","residual")
mmodel2df2 %>% group_by(Ethnicity)%>% summarise(sd(residual)) 

Again, the assumption is met.

#Programme part
mmodel2df3<-data.frame(full_data2$PROGRAMME_PART,mmodel2res) 
names(mmodel2df3)<-c("Programme_Part","residual")
mmodel2df3 %>% group_by(Programme_Part)%>% summarise(sd(residual))

Assumption satisfied again..

Moving onto checking the residuals of the model are normally distributed.

Normality of residuals

#now check for residuals for mmodel2
qqnorm(mmodel2res)

Confidence Intervals

#confint(mmodel2)

Research Question 3

Statistical Analysis

Mixed Model 3

# **Research Question 3**
# **3. Is there a link between assessment type, gender and student achievement?**

mmodel3<- lmer(MARK ~ ASSESSMENT_COMPONENT_TYPE_DESC * Gender + PROGRAMME_PART + (1|ID2) + (1|MODULE_CODE), data = full_data)
summary(mmodel3)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: MARK ~ ASSESSMENT_COMPONENT_TYPE_DESC * Gender + PROGRAMME_PART +  
    (1 | ID2) + (1 | MODULE_CODE)
   Data: full_data

REML criterion at convergence: 6689154

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-7.4574 -0.5327  0.0223  0.5787  4.7917 

Random effects:
 Groups      Name        Variance Std.Dev.
 ID2         (Intercept)  40.57    6.369  
 MODULE_CODE (Intercept)  25.44    5.044  
 Residual                153.51   12.390  
Number of obs: 840368, groups:  ID2, 31540; MODULE_CODE, 6945

Fixed effects:
                                                 Estimate Std. Error         df  t value
(Intercept)                                     6.729e+01  1.431e-01  1.236e+04  470.102
ASSESSMENT_COMPONENT_TYPE_DESCExam             -8.194e+00  4.717e-02  7.750e+05 -173.732
GenderWoman                                     8.882e-01  8.896e-02  3.505e+04    9.985
PROGRAMME_PARTB                                 2.593e-02  1.613e-01  1.548e+04    0.161
PROGRAMME_PARTC                                 7.156e-01  1.899e-01  1.292e+04    3.767
PROGRAMME_PARTD                                -1.454e+00  3.780e-01  3.142e+04   -3.846
PROGRAMME_PARTF                                 5.782e+00  5.344e-01  6.410e+03   10.819
PROGRAMME_PARTT                                -1.546e+00  1.887e-01  1.131e+04   -8.194
ASSESSMENT_COMPONENT_TYPE_DESCExam:GenderWoman  8.917e-01  6.904e-02  8.360e+05   12.916
                                               Pr(>|t|)    
(Intercept)                                     < 2e-16 ***
ASSESSMENT_COMPONENT_TYPE_DESCExam              < 2e-16 ***
GenderWoman                                     < 2e-16 ***
PROGRAMME_PARTB                                0.872332    
PROGRAMME_PARTC                                0.000166 ***
PROGRAMME_PARTD                                0.000120 ***
PROGRAMME_PARTF                                 < 2e-16 ***
PROGRAMME_PARTT                                 2.8e-16 ***
ASSESSMENT_COMPONENT_TYPE_DESCExam:GenderWoman  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
                                 (Intr) ASSESSMENT_COMPONENT_TYPE_DESCEx GndrWm
ASSESSMENT_COMPONENT_TYPE_DESCEx -0.117                                        
GenderWoman                      -0.245  0.104                                 
PROGRAMME_PARTB                  -0.580 -0.005                           -0.003
PROGRAMME_PARTC                  -0.557  0.000                            0.008
PROGRAMME_PARTD                  -0.297  0.006                            0.014
PROGRAMME_PARTF                  -0.230  0.005                            0.014
PROGRAMME_PARTT                  -0.696  0.036                           -0.023
ASSESSMENT_COMPONENT_TYPE_DESCE:  0.048 -0.467                           -0.209
                                 PROGRAMME_PARTB PROGRAMME_PARTC PROGRAMME_PARTD
ASSESSMENT_COMPONENT_TYPE_DESCEx                                                
GenderWoman                                                                     
PROGRAMME_PARTB                                                                 
PROGRAMME_PARTC                   0.510                                         
PROGRAMME_PARTD                   0.246           0.328                         
PROGRAMME_PARTF                   0.153           0.144           0.075         
PROGRAMME_PARTT                   0.444           0.430           0.255         
ASSESSMENT_COMPONENT_TYPE_DESCE:  0.003           0.000          -0.001         
                                 PROGRAMME_PARTF PROGRAMME_PARTT
ASSESSMENT_COMPONENT_TYPE_DESCEx                                
GenderWoman                                                     
PROGRAMME_PARTB                                                 
PROGRAMME_PARTC                                                 
PROGRAMME_PARTD                                                 
PROGRAMME_PARTF                                                 
PROGRAMME_PARTT                   0.170                         
ASSESSMENT_COMPONENT_TYPE_DESCE: -0.002           0.003         

This is interesting…shows gap in AT smaller for woman compared to men. Also average cw grade very similar for men and woman And woman perform better in exams compared to men And woman perform better in exams compared to men…

plot_model(mmodel3, type = "pred", terms = c("Gender", "ASSESSMENT_COMPONENT_TYPE_DESC"))

anova(mmodel3)
Type III Analysis of Variance Table with Satterthwaite's method
                                       Sum Sq Mean Sq NumDF  DenDF   F value    Pr(>F)    
ASSESSMENT_COMPONENT_TYPE_DESC        4858953 4858953     1 716363 31652.242 < 2.2e-16 ***
Gender                                  34941   34941     1  34071   227.611 < 2.2e-16 ***
PROGRAMME_PART                          44305    8861     5  12859    57.723 < 2.2e-16 ***
ASSESSMENT_COMPONENT_TYPE_DESC:Gender   25610   25610     1 835965   166.826 < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Again, similar to RQ1 & RQ2, all variables hold a significant effect on student performance

#effect size...look at video for how to explain this in report.
effectsize::eta_squared(mmodel3, partial = TRUE)
# Effect Size for ANOVA (Type III)

Parameter                             | Eta2 (partial) |       95% CI
---------------------------------------------------------------------
ASSESSMENT_COMPONENT_TYPE_DESC        |           0.04 | [0.05, 1.00]
Gender                                |       6.64e-03 | [0.01, 1.00]
PROGRAMME_PART                        |           0.02 | [0.02, 1.00]
ASSESSMENT_COMPONENT_TYPE_DESC:Gender |       2.00e-04 | [0.00, 1.00]

- One-sided CIs: upper bound fixed at [1.00].
#emmeans is meant to be looked at for main effect.
#in this case they were all significant so will do for each??

emmeans(mmodel3, list(pairwise~ASSESSMENT_COMPONENT_TYPE_DESC), adjust="tukey")
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 840368' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 840368' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
NOTE: Results may be misleading due to involvement in interactions
$`emmeans of ASSESSMENT_COMPONENT_TYPE_DESC`
 ASSESSMENT_COMPONENT_TYPE_DESC emmean     SE  df asymp.LCL asymp.UCL
 Coursework                      68.32 0.1268 Inf     68.07     68.57
 Exam                            60.57 0.1297 Inf     60.32     60.83

Results are averaged over the levels of: Gender, PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95 

$`pairwise differences of ASSESSMENT_COMPONENT_TYPE_DESC`
 1                 estimate     SE  df z.ratio p.value
 Coursework - Exam     7.75 0.0436 Inf 177.911  <.0001

Results are averaged over the levels of: Gender, PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
emmeans(mmodel3, list(pairwise~Gender), adjust="tukey")
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 840368' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 840368' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
NOTE: Results may be misleading due to involvement in interactions
$`emmeans of Gender`
 Gender emmean     SE  df asymp.LCL asymp.UCL
 Man     63.78 0.1301 Inf     63.53     64.04
 Woman   65.12 0.1376 Inf     64.85     65.38

Results are averaged over the levels of: ASSESSMENT_COMPONENT_TYPE_DESC, PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95 

$`pairwise differences of Gender`
 1           estimate     SE  df z.ratio p.value
 Man - Woman    -1.33 0.0884 Inf -15.087  <.0001

Results are averaged over the levels of: ASSESSMENT_COMPONENT_TYPE_DESC, PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
emmeans(mmodel3, list(pairwise~PROGRAMME_PART), adjust="tukey")
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 840368' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 840368' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
$`emmeans of PROGRAMME_PART`
 PROGRAMME_PART emmean     SE  df asymp.LCL asymp.UCL
 A               63.86 0.1384 Inf     63.59     64.13
 B               63.89 0.1354 Inf     63.62     64.15
 C               64.58 0.1584 Inf     64.27     64.89
 D               62.41 0.3612 Inf     61.70     63.12
 F               69.64 0.5200 Inf     68.62     70.66
 T               62.31 0.1309 Inf     62.06     62.57

Results are averaged over the levels of: ASSESSMENT_COMPONENT_TYPE_DESC, Gender 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95 

$`pairwise differences of PROGRAMME_PART`
 1     estimate    SE  df z.ratio p.value
 A - B  -0.0259 0.161 Inf  -0.161  1.0000
 A - C  -0.7155 0.190 Inf  -3.767  0.0023
 A - D   1.4538 0.378 Inf   3.846  0.0017
 A - F  -5.7823 0.534 Inf -10.819  <.0001
 A - T   1.5463 0.189 Inf   8.194  <.0001
 B - C  -0.6896 0.176 Inf  -3.927  0.0012
 B - D   1.4797 0.373 Inf   3.969  0.0010
 B - F  -5.7563 0.534 Inf -10.777  <.0001
 B - T   1.5723 0.186 Inf   8.450  <.0001
 C - D   2.1693 0.363 Inf   5.974  <.0001
 C - F  -5.0667 0.541 Inf  -9.370  <.0001
 C - T   2.2619 0.202 Inf  11.187  <.0001
 D - F  -7.2361 0.631 Inf -11.469  <.0001
 D - T   0.0925 0.377 Inf   0.245  0.9999
 F - T   7.3286 0.536 Inf  13.681  <.0001

Results are averaged over the levels of: ASSESSMENT_COMPONENT_TYPE_DESC, Gender 
Degrees-of-freedom method: asymptotic 
P value adjustment: tukey method for comparing a family of 6 estimates 
#There was an interaction so need emmeans for this
emmeans(mmodel3, pairwise ~ Gender | ASSESSMENT_COMPONENT_TYPE_DESC)
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'pbkrtest.limit = 840368' (or larger)
[or, globally, 'set emm_options(pbkrtest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
To enable adjustments, add the argument 'lmerTest.limit = 840368' (or larger)
[or, globally, 'set emm_options(lmerTest.limit = 840368)' or larger];
but be warned that this may result in large computation time and memory use.
$emmeans
ASSESSMENT_COMPONENT_TYPE_DESC = Coursework:
 Gender emmean     SE  df asymp.LCL asymp.UCL
 Man     67.88 0.1307 Inf     67.62     68.13
 Woman   68.77 0.1380 Inf     68.50     69.04

ASSESSMENT_COMPONENT_TYPE_DESC = Exam:
 Gender emmean     SE  df asymp.LCL asymp.UCL
 Man     59.68 0.1337 Inf     59.42     59.95
 Woman   61.46 0.1442 Inf     61.18     61.75

Results are averaged over the levels of: PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 
Confidence level used: 0.95 

$contrasts
ASSESSMENT_COMPONENT_TYPE_DESC = Coursework:
 contrast    estimate    SE  df z.ratio p.value
 Man - Woman   -0.888 0.089 Inf  -9.985  <.0001

ASSESSMENT_COMPONENT_TYPE_DESC = Exam:
 contrast    estimate    SE  df z.ratio p.value
 Man - Woman   -1.780 0.101 Inf -17.704  <.0001

Results are averaged over the levels of: PROGRAMME_PART 
Degrees-of-freedom method: asymptotic 

Evaluating mixed model-3 assumptions

Do not need to check linearity as each variable is categorical.

Homodscedasticity

#plot of mixed model 3 residuals
plot(mmodel3)

boxplot(mmodel3@resp$wtres~full_data$ASSESSMENT_COMPONENT_TYPE_DESC)

boxplot(mmodel3@resp$wtres~full_data$Gender)

boxplot(mmodel3@resp$wtres~full_data$PROGRAMME_PART)

Another way to check for homeoscedasticity - (sd) residuals

#ASSESSMENT TYPE
mmodel3res<-mmodel3@resp$wtres
#assessment type residuals in df
mmodel3df<-data.frame(full_data$ASSESSMENT_COMPONENT_TYPE_DESC,mmodel3res) 
names(mmodel3df)<-c("ASSESSMENT_COMPONENT_TYPE_DESC","residual")
mmodel3df %>% group_by(ASSESSMENT_COMPONENT_TYPE_DESC)%>% summarise(sd(residual))
#Gender
mmodel3df2<-data.frame(full_data$Gender,mmodel3res) 
names(mmodel3df2)<-c("Gender","residual")
mmodel3df2 %>% group_by(Gender)%>% summarise(sd(residual)) 
#Programme part
mmodel3df3<-data.frame(full_data$PROGRAMME_PART,mmodel3res) 
names(mmodel3df3)<-c("Programme_Part","residual")
mmodel3df3 %>% group_by(Programme_Part)%>% summarise(sd(residual))

Assumptions met for all variables

Normality of residuals

qqnorm(mmodel3res)

All assumptions for mixed model 3 met.

Confidence Intervals

#confint(mmodel3)
---
title: "Data summary and statistical analysis notebook"
author: "Subeida Hassan"
date: "26th August 2022"
output:
  html_document:
    toc: yes
    toc_depth: '3'
    df_print: paged
  pdf_document:
    toc: yes
    toc_depth: 3
  html_notebook:
    self_contained: yes
    highlight: textmate
    toc: yes
    toc_depth: 3
    number_sections: no
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  comment = "",
  results = "hold",
  echo = TRUE,
  message = FALSE,
  warning = FALSE,
  fig.width = 4,
  fig.height = 2.6,
  fig.align = "center"
) 
```

Loading required packages
```{r, message = FALSE,comments=FALSE, warning = FALSE}
#importing libraries
library("tidyverse")
library(here)
library(ggrepel)
library(lme4)
library(lmerTest)
library(sjstats)
library(emmeans)
library(MuMIn)
library(sjPlot)
library(sjmisc)
library("car")
```

# **Preliminary analysis**
Reading data into data frame
```{r}
#Ethnicity data set
ethnicity_data <- read_delim(file = here("data", "qry_Ethnic_origin_2.txt"), delim = ",")
head(ethnicity_data) #previewing first 5 lines

#Mark data set
mark_data <- read_delim(file = here("data", "qry_Mark_Data_2.txt"), delim = ",")
head(mark_data) #previewing first 5 lines
```

## Ethnicity Dataset
### Cleaning 
```{r}
#checking for missing data
ethnicity_data %>% sapply(function(x) sum(is.na(x)) )

#removing missing data
clean_ethnicity_data <- ethnicity_data %>%
  drop_na()

#checking if na's removed.
clean_ethnicity_data %>% sapply(function(x) sum(is.na(x)))

```

```{r}
#checking for duplicates & removing
length(unique(clean_ethnicity_data$ID2)) #number of students in ethnic data
length(unique(clean_ethnicity_data$ID2)) == nrow(clean_ethnicity_data) #returns TRUE as no duplicates

```

```{r}
#renaming columns & saving in new data frame 
clean_ethnicity_data %>%
  rename(
    Gender = GENDER_DESC,
    Ethnicity = ETHNIC_ORIGIN_DESC
    ) -> clean_ethnicity_data
```

```{r}
#Computing percentage of students belonging to each gender/ethnic group

clean_ethnicity_data %>%
  group_by(Gender) %>%
  tally () %>%
  mutate(freq = (n / sum(n))*100) %>%
  mutate(percentage = scales::label_percent()(n / sum(n))) %>%
  rename("Number of students" = n)

#Categorising into man vs woman other levels to little cases
#remove non binary, other & prefer not to say
clean_ethnicity_data<-clean_ethnicity_data[!(clean_ethnicity_data$Gender=="Non-Binary" | clean_ethnicity_data$Gender=="Prefer not to say" | clean_ethnicity_data$Gender=="Other") ,]


#check levels of ethnicites
clean_ethnicity_data %>%
  group_by(Ethnicity) %>%
  tally () %>%
  mutate(freq = (n / sum(n))*100) %>%
  mutate(percentage = scales::label_percent()(n / sum(n)))
```

I will categorise ethnicity differently depending on research question. Firstly, I will begin with white vs other.

```{r}
#duplicating data set at this point as will categorise differently for second research question
clean_ethnicity_data -> clean_ethnicity_data2
```

```{r}
#categorising ethnicities into white vs non white
#rename all white columns- white.
#rename all other columns other.

clean_ethnicity_data[clean_ethnicity_data == "White - British"] <- "White"
clean_ethnicity_data[clean_ethnicity_data == "White - Irish"] <- "White"
clean_ethnicity_data[clean_ethnicity_data == "White - Scottish"] <- "White"
clean_ethnicity_data[clean_ethnicity_data == "Other White Background"] <- "White"

clean_ethnicity_data[clean_ethnicity_data == "Arab"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Asian Or Asian British - Bangladeshi"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Asian Or Asian British - Indian"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Asian Or Asian British - Pakistani"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Black Or Black British - African"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Black Or Black British - Caribbean"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Chinese"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Gypsy or Traveller"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Mixed - White And Asian"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Mixed - White And Black African"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Mixed - White And Black Caribbean"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Other Asian Background"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Other Black Background"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Other Ethnic Background"] <- "BAME"
clean_ethnicity_data[clean_ethnicity_data == "Other Mixed Background"] <- "BAME"

#remove not known,no information & info refused- as could be any ethnicity.

clean_ethnicity_data<-clean_ethnicity_data[!(clean_ethnicity_data$Ethnicity=="Information Refused" | clean_ethnicity_data$Ethnicity=="Information Refused - OBSOLETE VALUE" | clean_ethnicity_data$Ethnicity=="No Information" | clean_ethnicity_data$Ethnicity=="Not Known") ,]

```

```{r}
#changing numerical variables to factors. 
clean_ethnicity_data %>%
  mutate_at(vars(Gender, Ethnicity), list(factor)) -> clean_ethnicity_data

summary(clean_ethnicity_data)

```

### Visualisation
```{r}
#Ethnicity
#Visualising number of students in each ethnicity category 

ethnicity_data %>%
  group_by(ETHNIC_ORIGIN_DESC) %>%
  summarise(counts = n()) %>%
  mutate(freq = (counts / sum(counts))*100) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(., aes(x = ETHNIC_ORIGIN_DESC, y = counts)) +
  geom_bar(fill = "#0073C2FF", stat = "identity") +
  #geom_text(aes(label = percentage), angle = 60) +
  theme(axis.text.x = element_text(angle = 60, 
                                   hjust = 1)) +
  ggtitle("Number of students in each ethnic category") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Ethnicity') +
  ylab('Number of students') 
```

```{r}
#Ethnicity
#Percentage of students belonging to each ethnicity after categorisation
clean_ethnicity_data %>%
  group_by(Ethnicity) %>%
  tally () %>%
  mutate(freq = scales::label_percent()(n / sum(n)))  %>%
  ggplot( ., aes(x="", y=freq, fill=Ethnicity)) +
  geom_bar(stat="identity", width=1, color="white") +
  coord_polar("y", start=0) +
  theme_void() +
  geom_text(aes(label = freq), color = "white", position = position_stack(vjust = 0.5), size = 4) +
  scale_fill_brewer(palette = "Paired") +
  ggtitle("Percentage of students in each ethnic category") +
  theme(plot.title = element_text(hjust = 0.5)) 
```

```{r}
#GENDER
#Percentage of students belonging to each gender category
ethnicity_data %>%
  group_by(GENDER_DESC) %>%
  summarise(counts = n()) %>%
  mutate(freq = (counts / sum(counts))*100) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(., aes(x = GENDER_DESC, y = counts, fill=GENDER_DESC)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = percentage), vjust = -0.5) + 
  scale_fill_brewer(palette = "Paired") +
  ggtitle("Percentage of students in each gender category") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Gender') +
  ylab('Number of students') +
  guides(fill = guide_legend(title = "Gender Description ")) 
```

```{r}
#GENDER
#Percentage of students belonging to each gender after categorisation
clean_ethnicity_data %>%
  group_by(Gender) %>%
  tally () %>%
  mutate(freq = scales::label_percent()(n / sum(n))) %>%
  ggplot( ., aes(x="", y=freq, fill=Gender)) +
  geom_bar(stat="identity", width=1, color="white") +
  coord_polar("y", start=0) +
  theme_void() +
  geom_text(aes(label = freq), color = "white", position = position_stack(vjust = 0.5), size = 5) +
  scale_fill_brewer(palette = "Set2") +
  ggtitle("Percentage of students in each gender category") +
  theme(plot.title = element_text(hjust = 0.5)) 
```

## Mark Dataset
### Cleaning 
```{r}
#summary of mark data
summary(mark_data)
```

```{r}
#checking for missing data?
mark_data %>% sapply(function(x) sum(is.na(x)) )

#remove missing data? - decided to not remove missing data as only missing data in programme school, module school and part_end_date and module mark- i am not directly assessing these columns- so doesn't matter too much. If looking at effect of school on mark may need to consider removing missing values. 

#checking for duplicates
length(unique(mark_data$ID2)) #number of students in marks data- same as ethnicity data set
length(unique(mark_data$ID2)) == nrow(mark_data) #returns FALSE as there are duplicates

#removing duplicate rows- 1,114,911 rows once duplicates removed.
distinct(mark_data) -> clean_mark_data
```

```{r}
#changing variables to factors.
clean_mark_data %>%
  mutate_at(vars(PROGRAMME_SCHOOL, PROGRAMME_OWNER, PROGRAMME_LEVEL_DESC, PROGRAMME_PART, PART_END_DATE, MODULE_OWNER, MODULE_SCHOOL, MODULE_CODE, MODULE_LONG_TITLE, INCLUDE_MARK_DESC, ASSESSMENT_COMPONENT_CODE, ASSESSMENT_COMPONENT_TITLE, ASSESSMENT_COMPONENT_TYPE_DESC), list(factor)) -> clean_mark_data
```


### Visualisation
```{r}
#how many years of data?
clean_mark_data %>%
  group_by(STUDENT_START_YEAR) %>%
  count ()
#data spans 2007-2022
#Only interested in the last 5 years, as before 2016, a lot less data/outdated.
#mark data only has a single students results for 2022, therefore, only one gender, one ethnicity.
#This is not good enough for any analysis on students performance, will cause serious over generalisation.


#remove data before 2017 & after 2021
clean_mark_data %>%
  filter(STUDENT_START_YEAR >= 17 & STUDENT_START_YEAR <= 21) ->clean_mark_data

#Visualisation of students in each year group.
clean_mark_data %>%
  group_by(STUDENT_START_YEAR) %>% 
  count() %>%
  ggplot(., aes(x = STUDENT_START_YEAR, y= n)) + 
  geom_bar(stat = "identity", fill = "darkgreen", size = 2.5) +
  geom_text(aes(label = n), vjust = -0.5) +
  ggtitle("Number of students in each year (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Student Start Year') +
  ylab('Number of students')  
```

```{r}
#How many assessment types in data set?
clean_mark_data %>%
  group_by(ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  count()
#Coursework, Exam, In-Class Test, In-Dept Exam, Laboratory Test

#types of assessment types & relative percentages of students who did the AT
clean_mark_data %>%
  group_by(ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  summarise(counts = n()) %>%
  mutate(freq = (counts / sum(counts))) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot( ., aes(x="", y=freq, fill=ASSESSMENT_COMPONENT_TYPE_DESC)) +
  geom_bar(stat="identity", width=1, color="white") +
  coord_polar("y", start=180) +
  theme_void() +
  geom_text(aes(label = percentage), position = position_stack(vjust = 0.5), size = 3) +
  scale_fill_brewer(palette = "Spectral") +
  guides(fill = guide_legend(title = "Assessment type")) +
  ggtitle("Percentage of students in each assessment type") +
  theme(plot.title = element_text(hjust = 0.5)) 
```

```{r}
#categorise into exam and cw only. 
#So- exam = Exam, In-Dept Exam, cw = CW, In-Class Test and Lab Test

clean_mark_data[clean_mark_data == "In-Dept Exam"] <- "Exam"
clean_mark_data[clean_mark_data == "In-Class Test"] <- "Coursework"
clean_mark_data[clean_mark_data == "Laboratory Test"] <- "Coursework"

#dropping levels 
#in dept exam, in class test and lab test
droplevels(clean_mark_data$ASSESSMENT_COMPONENT_TYPE_DESC) -> clean_mark_data$ASSESSMENT_COMPONENT_TYPE_DESC

summary(clean_mark_data$ASSESSMENT_COMPONENT_TYPE_DESC)
```

```{r}
#visualising percentage of students under each assessment type after categorisation
clean_mark_data %>%
  group_by(ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  summarise(counts = n()) %>%
  mutate(freq = (counts / sum(counts))) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot( ., aes(x="", y=freq, fill=ASSESSMENT_COMPONENT_TYPE_DESC)) +
  geom_bar(stat="identity", width=1, color="white") +
  coord_polar("y", start=0) +
  theme_void() +
  geom_text(aes(label = percentage), position = position_stack(vjust = 0.5), size = 3) +
  scale_fill_brewer(palette = "Spectral") +
  guides(fill = guide_legend(title = "Assessment type")) +
  ggtitle("Percentage of students in each assessment type") +
  theme(plot.title = element_text(hjust = 0.5)) 
```

```{r}
#cleaning programme part
summary(clean_mark_data$PROGRAMME_PART)

#visualise number of students in each programme part
ggplot(clean_mark_data, aes(x = PROGRAMME_PART)) + 
  geom_bar(fill = "blue") +
theme(axis.text.x = element_text(angle = 60, 
                                   hjust = 1)) +
  ggtitle("Number of students in each part") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Programme Part') +
  ylab('Number of students')  
```
As can be seen from the above table- there are many parts- but in this study i am only interested in UG and PG students, so will remove all cases where not A, B, C, D, F, and T. 
```{r}
#There are 16 parts- want to keep 6 and remove 10

subset(clean_mark_data, PROGRAMME_PART != "G" & PROGRAMME_PART != "I" & PROGRAMME_PART != "R" & PROGRAMME_PART != "R0" & PROGRAMME_PART != "R1" & PROGRAMME_PART != "R2" & PROGRAMME_PART != "RT" & PROGRAMME_PART != "TO" & PROGRAMME_PART != "TR" & PROGRAMME_PART != "TX") -> clean_mark_data

```

```{r}
#dropping levels with 0 
summary(clean_mark_data$PROGRAMME_PART)

droplevels(clean_mark_data$PROGRAMME_PART) -> clean_mark_data$PROGRAMME_PART

summary(clean_mark_data$PROGRAMME_PART)
```

```{r}
#visualise number of students in each programme parts we are interested in
ggplot(clean_mark_data, aes(x = PROGRAMME_PART)) + 
  geom_bar(fill = "blue") +
theme(axis.text.x = element_text(angle = 60, 
                                   hjust = 1)) +
   ggtitle("Number of students in each part") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Programme Part') +
  ylab('Number of students') 
```

```{r}
#distribution of marks in cleaned mark dataset
hist(clean_mark_data$MARK, 
     main="Histogram of mark (2017-2021)",
xlab="Mark",
xlim=c(0,100),
col="darkmagenta")
#seems close to normal distribution
```

```{r}
#from above looks like there are marks below 0
clean_mark_data %>%
  group_by(MARK) %>%
  tally ()
#REMOVE MARKS BELOW, ONE ENTRY OF -3. 
filter(clean_mark_data, MARK > 0) -> clean_mark_data
#now 870021 rows
```

# **Joining the two datasets**
```{r}
#JOIN ETHNIC DATA AND MARK DATA
inner_join(clean_ethnicity_data, clean_mark_data) -> full_data
print(distinct(full_data))
```

# **Research Question 1**
# **1.	What affect does the assessment type have on the student performance and the BAME awarding gap?**

This is a multi-leveled question. I will began by looking at the effect of ethnicity and assessment type separately on student performance.

## 1.1 Effect of Ethnicity on student performance

Variance in marks for white vs other
```{r}
boxplot(MARK~Ethnicity, data = full_data)
```
looks like a lot of data points are outliers- but data is over 5 years and we are mostly interested in top grades so will filter these out later. 

Analysis of average Marks of White vs BAME/Other students (2017-2021)

Below is bar chart showing of effect of ethnicity on mean mark between 2017-2021

```{r}
full_data %>%
  select(Ethnicity, MARK) %>%
  group_by(Ethnicity) %>%
  tally(mean(MARK))  %>%
  rename(Average_mark = n) %>%
  ggplot(aes(x=Ethnicity, y = Average_mark, fill =Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Paired") +
  geom_text(aes(label = round(Average_mark, 2)), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Average Test Scores by Ethnicity between 2017-2021") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ylab('Average Mark') 

```
Next, I wanted to see the affect of ethnicity on average mark each year between 2017-2021.
This table below shows the gap in average grade in white vs Bame students is reducing over the years. This reduction is very small changing by just 1.4 over 5 years. 
```{r}
#White vs non white grades table like in screenshot in report
full_data %>%
  select(INSTANCE_ACADEMIC_YEAR, Ethnicity,MARK) %>%
  group_by(INSTANCE_ACADEMIC_YEAR, Ethnicity) %>%
  tally(mean(MARK)) %>%
  rename(Average_mark = n) %>%
  spread(key = Ethnicity, value = Average_mark) %>%
  mutate(Gap = White - BAME)
```

Below is a line graph to visualise the above table. This is showing gap is reducing, but both white and bame students average grade seems to be reducing over the years. 
```{r}
full_data %>%
  select(INSTANCE_ACADEMIC_YEAR, Ethnicity,MARK) %>%
  group_by(INSTANCE_ACADEMIC_YEAR, Ethnicity) %>%
  tally(mean(MARK)) %>%
  rename(Average_mark = n) %>%
  #spread(key = Ethnicity, value = Average_mark) %>%
 # mutate(Gap = White - BAME) %>%
  ggplot(aes(x = INSTANCE_ACADEMIC_YEAR, y = Average_mark, col = Ethnicity)) +
  geom_line() +
  geom_point() +
  ggtitle("Average grade for white vs BAME students (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Academic Year') +
  ylab('Average Mark') +
  labs(col='Ethnicity') 
```
### Analysis of BAME Awarding Gap (2017-2021)
The below bar graph is showing percentage of white vs BAME students achieving top degree (2017-2021)
NOTE- for methodology/results
I grouped by ID2 first in order to calculate average grade for each student..then i filtered for students who got an average grade above 60
Then grouped by ethnicity
This is important as the data is not important, if not grouped by ID2 results would show any student who got above 60 for at least 1 assessment

```{r}
full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(Average_mark >=60) %>%
  select(ID2, Ethnicity, Average_mark) %>%
  distinct() %>%
  group_by(Ethnicity) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=Ethnicity, y = counts, fill =Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Pastel1") +
  geom_text(aes(label = percentage), position = position_stack(vjust = 1.1), size = 3)  +
  ggtitle("Percentage of white vs BAME students achieving top degree (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ylab('Number of students')
```

Below graph shows difference between number of White vs BAME students achieving top degree (BAME awarding gap) is consistent over years 2017-2021
```{r}
#Visualising percentage of white vs BAME students achieving top degree each year (2017-2021)
full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(Average_mark >=60) %>%
  select(ID2, Ethnicity, Average_mark, INSTANCE_ACADEMIC_YEAR) %>%
  distinct() %>%
  group_by(INSTANCE_ACADEMIC_YEAR, Ethnicity) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=INSTANCE_ACADEMIC_YEAR, y = counts, fill =Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Pastel1") +
  geom_text(aes(label = percentage), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Percentage of white vs BAME students achieving top degree each year (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Academic Year') +
  ylab('Number of students')
```
Problem with above graphs is that there are a lot more white students in the university, and therefore it is a given that more white students are achieving top degrees. 
So instead, this graph clearly shows distribution of degree outcomes instead.The following graph makes the results clearer for interpretation/comparisons.
For example here, 37% of white students get a first in comparison to only 21.9% of BAME students.

```{r}
#visualisation of distribution of degree outcomes
full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  mutate(Grade = case_when(Average_mark >= 70 ~ "First Class",
                           Average_mark >= 60 ~ "Upper Second Class Honour",
                           Average_mark >= 50 ~ "Lower Second Class Honour",
                           Average_mark >= 40 ~ "Third class/pass",
                           Average_mark < 40 ~ "Fail")) %>%
  group_by(Ethnicity, Grade) %>%
  summarise(counts = n()) %>%
  mutate(freq = (counts / sum(counts))) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=Ethnicity, y = freq, fill =Grade)) +
  geom_bar(stat = 'identity') + 
  coord_flip() +
  scale_fill_brewer(palette = "Pastel1") +
  geom_text_repel(aes(label = percentage), position = position_stack(vjust = 0.5), size = 2.5) +
  ggtitle("Distribution of degree outcomes by ethnic group (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  labs(y = "Frequency", fill = "Degree Classification")
```

## 1.2 Effect of assessment type on student performance

I have began by visualising the average mark for each assessment type (2017-2021)
```{r}
#Visualisation of effect of assessment type on average mark between 2017-2021
full_data %>%
  select(ASSESSMENT_COMPONENT_TYPE_DESC, MARK) %>%
  group_by(ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  tally(mean(MARK))  %>%
  rename(Average_mark = n) %>%
   #spread(key = Ethnicity, value = Average_mark) %>%
  ggplot(aes(x=ASSESSMENT_COMPONENT_TYPE_DESC, y = Average_mark, fill = ASSESSMENT_COMPONENT_TYPE_DESC)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Pastel2") +
  geom_text(aes(label = round(Average_mark, 2)), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Average Mark for Coursework vs Exam (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Assessment Type') +
  ylab('Average Mark') +
  guides(fill = guide_legend(title = "Assessment type")) 
```
This bar graph above is showing that students overall perform much better in coursework than exams...but does ethnicity have an effect on this?? Will look at this later

Next i wanted to evaluate the average yearly mark dependent on assessment type. This shows every year the average grade is much bigger for cw type assessments vs exams. Essentially students perform much better in coursework type assessments in comparison to exams. 
```{r}
full_data %>%
  select(INSTANCE_ACADEMIC_YEAR, ASSESSMENT_COMPONENT_TYPE_DESC,MARK) %>%
  group_by(INSTANCE_ACADEMIC_YEAR, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  tally(mean(MARK)) %>%
  rename(Average_mark = n) %>%
  spread(key = ASSESSMENT_COMPONENT_TYPE_DESC, value = Average_mark) %>%
  mutate(Gap = Coursework - Exam)
```

This line graph below shows many things:
1. students perform much better in cw vs exams
2. Average cw mark is reducing over the years
3. Average exam mark is increasing over the years, but goes down in 2021
4. assessment awarding gap is reducing. 
```{r}
#Visualisation of above
full_data %>%
  select(INSTANCE_ACADEMIC_YEAR, ASSESSMENT_COMPONENT_TYPE_DESC,MARK) %>%
  group_by(INSTANCE_ACADEMIC_YEAR, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  tally(mean(MARK)) %>%
  rename(Average_mark = n) %>%
  #spread(key = ASSESSMENT_COMPONENT_TYPE_DESC, value = Average_mark) %>%
  #mutate(Gap = Coursework - Exam) %>%
  ggplot(aes(x = INSTANCE_ACADEMIC_YEAR, y = Average_mark, col = ASSESSMENT_COMPONENT_TYPE_DESC)) +
  geom_line() +
  geom_point() +
  ggtitle("Average yearly Mark for Coursework vs Exam (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Academic Year') +
  ylab('Average Mark') +
  labs(col='Assessment Type') 

```

Analysis of effect of assessment type on achieving top degree(2017-2022)

What about effect of assessment type on top degrees?
This bar chart shows the average number of students that did cw vs exam in all students that achieved a top degree. 

```{r}
#average percentage of students who did cw vs exam to get first
full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(Average_mark >=60) %>%
  select(ID2, ASSESSMENT_COMPONENT_TYPE_DESC, Average_mark) %>%
  distinct() %>%
  group_by(ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=ASSESSMENT_COMPONENT_TYPE_DESC, y = counts, fill = ASSESSMENT_COMPONENT_TYPE_DESC)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Pastel2") +
  geom_text(aes(label = percentage), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Percentage of students doing cw vs exam for top degree") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Assessment Type') +
  ylab('Number of students') +
  guides(fill = guide_legend(title = "Assessment type")) 
```

This bar chart is showing yearly average percentage of students who did cw vs exam to get first
This results are pretty consistent over the years. Has reduced slightly over the years.
Gap in 21 looks pretty small, but number of students in this year was lower. 

```{r}
#percentage of students who did cw vs exam to get first (2017-2022)
  full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(Average_mark >=60) %>%
  select(ID2, ASSESSMENT_COMPONENT_TYPE_DESC, Average_mark, INSTANCE_ACADEMIC_YEAR) %>%
  distinct() %>%
  group_by(INSTANCE_ACADEMIC_YEAR, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=INSTANCE_ACADEMIC_YEAR, y = counts, fill =ASSESSMENT_COMPONENT_TYPE_DESC)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Pastel1") +
  geom_text(aes(label = percentage), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Percentage of students doing cw vs exam for top degree each year") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Academic Year') +
  ylab('Number of students') +
  guides(fill = guide_legend(title = "Assessment type")) 
```

## 1.3 Analysing effect of assessment type on student performance and BAME awarding gap

```{r}
#Checking there is a reasonable number of white/others in each assessment type. 
full_data %>%
  select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  group_by(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  tally()
```
First i want to look at affect of assessment type on student performance(White vs Other)
This table below shows the difference in avgerage cw and exam marks for white Vs Bame students 

Average grade over past 5 years for assessment type (AT) and ethnicity
```{r}
full_data %>%
  select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC,MARK) %>%
  group_by(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  tally(mean(MARK))  %>%
  rename(Average_mark = n) %>%
   spread(key = Ethnicity, value = Average_mark) %>%
  mutate(gap = White - BAME)
```
The gap doesnt look huge, but it is bigger for coursework type assessments. However this is gap in average mark (student performance), not the number of students who achieved a top degree. Therefore, this is not relating to Bame awarding gap. Still interesting...

Visualisation of above table
Looking at average grade over past 5 years for AT and ethnicity
```{r}
full_data %>%
  select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC,MARK) %>%
  group_by(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  tally(mean(MARK))  %>%
  rename(Average_mark = n) %>%
   #spread(key = Ethnicity, value = Average_mark) %>%
  ggplot(aes(x=ASSESSMENT_COMPONENT_TYPE_DESC, y = Average_mark, fill = Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge')  + 
  scale_fill_brewer(palette = "Accent") +
  geom_text(aes(label = round(Average_mark, 2)), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Average Mark By Assessment Type And Ethnicity") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Assessment Type') 
  ylab('Average Mark')

```
This graph above is useful as it shows difference in average mark for each assessment type by ethnicity.
However this research question pertains to the affect of assessment type on BAME awarding gaps.
Therefore, instead of average marks, we need to compare the number of white vs BAME students who achieved top degrees and see if gap is greater for cw type assessments or coursework. 

```{r}
full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(Average_mark >=60) %>%
  select(ID2, Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC, Average_mark) %>%
  distinct() %>%
  select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  group_by(Ethnicity,ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  #select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC, percentage) %>%
  ggplot(aes(x=ASSESSMENT_COMPONENT_TYPE_DESC, y = percentage, fill = Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge')  + 
  scale_fill_brewer(palette = "Set1") +
  geom_text(aes(label = percentage), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Effect of assessment type on awarding gap") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Assessment Type') +
  ylab('Percentage of students')
```
The results here are interesting. There is a 4% awarding gap for both ethnicities in each assessment type- this suggests exam do not lead to a wider awarding gap for the years 2017-2021.White students still performing better for both assessment types, but both ethnicities performing similar for assessment type. 

```{r}
#interaction plot of full dataset. 
interaction.plot(x.factor = full_data$Ethnicity, #x-axis variable
                 trace.factor = full_data$ASSESSMENT_COMPONENT_TYPE_DESC, #variable for lines
                 response = full_data$MARK, #y-axis variable
                 fun = median, #metric to plot
                 ylab = "Average Mark",
                 xlab = "Ethnicity",
                 col = c("red", "blue"),
                 lty = 1, #line type
                 lwd = 2, #line width
                 trace.label = "Assessment type")
```

# Statistical Analysis/Modelling

## Mixed model 1

```{r}
#Question 1:
#	1.	What affect does the assessment type have on student performance?

#fitting model
mmodel1 <- lmer(MARK ~ ASSESSMENT_COMPONENT_TYPE_DESC * Ethnicity + PROGRAMME_PART + (1|ID2) + (1|MODULE_CODE), data = full_data)
```

```{r}
#showing model results
summary(mmodel1)
```


```{r}
#interaction plot of model...

plot_model(mmodel1, type = "pred", terms = c("Ethnicity", "ASSESSMENT_COMPONENT_TYPE_DESC"))
```

```{r}
anova(mmodel1)
```
Results from the anova test show each of these variables have significant effect on mark

```{r}
#effect size
effectsize::eta_squared(mmodel1, partial = TRUE)
```

```{r}
#emmeans is meant to be looked at for main effect.
#in this case they were all significant so will do for each

emmeans(mmodel1, list(pairwise~ASSESSMENT_COMPONENT_TYPE_DESC), adjust="tukey")

#here comparison shows that there is a significant difference in mark when assessment type is cw compared to exam (p.value = <0.0001)
```

```{r}
#computing effect sizes from emmean- firstly effect of assessment type
#code adapted from https://search.r-project.org/CRAN/refmans/emmeans/html/eff_size.html
VarCorr(mmodel1)
totSD <- sqrt( 6.2315 + 5.0433 +  12.3898)
emmV <- emmeans(mmodel1, ~ ASSESSMENT_COMPONENT_TYPE_DESC)
eff_size(emmV, sigma = totSD, edf = 5)
```

```{r}
emmeans(mmodel1, list(pairwise~Ethnicity), adjust="tukey")
```

```{r}
#effect size of ethnicity
emmV2 <- emmeans(mmodel1, ~ Ethnicity)
eff_size(emmV2, sigma = totSD, edf = 5)
```


```{r}
#effect size of programme part
emmeans(mmodel1, list(pairwise~PROGRAMME_PART), adjust="tukey")
#interesting as only some parts show sig difference on mark... some parts e.g. B,T, show no sig diff on mark?? wonder why?? is this correct interpretation??
```

```{r}
#There was an interaction so computing emmeans for this
emmeans(mmodel1, pairwise ~ Ethnicity | ASSESSMENT_COMPONENT_TYPE_DESC)
```

```{r}
#effect size of interaction 
emmV3 <- emmeans(mmodel1, ~ Ethnicity | ASSESSMENT_COMPONENT_TYPE_DESC)
eff_size(emmV3, sigma = totSD, edf = 5)
```


## Evaluating mixed model-1 assumptions

### linearity
Usually, there are 3 model assumptions which we need to check; linearity, homoscedasticity and normality of residuals.
We do not need to look at linearity as all variables in this model are categorical.

### Homoscedasticity:
```{r}
#code adapted from https://ademos.people.uic.edu/Chapter18.html
plot(mmodel1)
```

```{r}
#boxplot of residuals to assess distribution of residuals
boxplot(mmodel1@resp$wtres~full_data$ASSESSMENT_COMPONENT_TYPE_DESC)
```

```{r}
boxplot(mmodel1@resp$wtres~full_data$Ethnicity)
```

```{r}
boxplot(mmodel1@resp$wtres~full_data$PROGRAMME_PART)
```
Another way to check for homoscedasticity - (sd) residuals
```{r}
#ASSESSMENT TYPE
mmodel1res<-mmodel1@resp$wtres
#assessment type residuals in df
mmodel1df<-data.frame(full_data$ASSESSMENT_COMPONENT_TYPE_DESC,mmodel1res) 
names(mmodel1df)<-c("ASSESSMENT_COMPONENT_TYPE_DESC","residual")
mmodel1df %>% group_by(ASSESSMENT_COMPONENT_TYPE_DESC)%>% summarise(sd(residual)) 
```

Since the highest sd is less than twice the lowest, that is fine (according to general rule of thumb for homoscedasticity across groups of a factor)

Now we will check for the remaining variables...
```{r}
#Ethnicity
mmodel1df2<-data.frame(full_data$Ethnicity,mmodel1res) 
names(mmodel1df2)<-c("Ethnicity","residual")
mmodel1df2 %>% group_by(Ethnicity)%>% summarise(sd(residual)) 

#assumption satisfied 
```

```{r}
#programme part
mmodel1df3<-data.frame(full_data$PROGRAMME_PART,mmodel1res) 
names(mmodel1df3)<-c("Programme_Part","residual")
mmodel1df3 %>% group_by(Programme_Part)%>% summarise(sd(residual))

#assumption met for this categorical variable as the largest residual is 15.3 and is less than 2* the smallest residual of D (9.20)
```
### normality of residuals

```{r}
qqnorm(mmodel1res)
```

### Confidence intervals
```{r}
#Interval estimates for mixed models
#confint(mmodel1)
```

The above model is assessing effect of variables on student performance by looking at average marks. We want to look at awarding gaps so will categorise the marks into top degree and below and run a binomial model.

## Generalised linear mixed model 1.2

```{r}
#creating binomial response variable
full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  mutate(Grade = case_when(Average_mark >= 60 ~ 1,
                           Average_mark < 60 ~ 0)) -> full_data3

```

```{r}
#1.	What affect does the assessment type have on the BAME awarding gap?
linmod<-glmer(as.factor(Grade) ~ ASSESSMENT_COMPONENT_TYPE_DESC * Ethnicity + PROGRAMME_PART + (1|ID2) + (1|MODULE_CODE), family="binomial",data = full_data3, nAGQ=0)
```

```{r}
summary(linmod)
```

```{r}
#calculating odds ratios
se <- sqrt(diag(vcov(linmod)))
# table of estimates with 95% CI
(tab <- cbind(Est = fixef(linmod), LL = fixef(linmod) - 1.96 * se, UL = fixef(linmod) + 1.96 *
    se))
```

```{r}
#odd ratios
exp(tab)
```

```{r}
#evaluating model significance/quality 
#Analysis of Deviance test
Anova(linmod)
```

```{r}
#interaction plot from model 1.2
plot_model(linmod, type = "pred", terms = c("Ethnicity", "ASSESSMENT_COMPONENT_TYPE_DESC"))
```

```{r}
emmeans(linmod, ~ ASSESSMENT_COMPONENT_TYPE_DESC, type = "response")
```

```{r}
emmeans(linmod, ~ Ethnicity, type = "response")
```


```{r}
emmeans(linmod, ~ Ethnicity | ASSESSMENT_COMPONENT_TYPE_DESC, type = "response")
```

This model does not require checking of assumptions

# **Research Question 2**
# **2.	What affect does the assessment type have on the performance of Non-White students (Namely Asian, Black, Chinese, and mixed) and the awarding gap?**

Data wrangling:
This research question required different categorisisation of ethnicities. 
```{r}
#categorising ethnicities into Asian, Black, Chinese, Mixed & White

clean_ethnicity_data2[clean_ethnicity_data2 == "White - British"] <- "White"
clean_ethnicity_data2[clean_ethnicity_data2 == "White - Irish"] <- "White"
clean_ethnicity_data2[clean_ethnicity_data2 == "White - Scottish"] <- "White"
clean_ethnicity_data2[clean_ethnicity_data2 == "Other White Background"] <- "White"

clean_ethnicity_data2[clean_ethnicity_data2 == "Asian Or Asian British - Bangladeshi"] <- "Asian"
clean_ethnicity_data2[clean_ethnicity_data2 == "Asian Or Asian British - Indian"] <- "Asian"
clean_ethnicity_data2[clean_ethnicity_data2 == "Asian Or Asian British - Pakistani"] <- "Asian"
clean_ethnicity_data2[clean_ethnicity_data2 == "Black Or Black British - African"] <- "Black"
clean_ethnicity_data2[clean_ethnicity_data2 == "Black Or Black British - Caribbean"] <- "Black"
clean_ethnicity_data2[clean_ethnicity_data2 == "Chinese"] <- "Chinese"
clean_ethnicity_data2[clean_ethnicity_data2 == "Mixed - White And Asian"] <- "Mixed"
clean_ethnicity_data2[clean_ethnicity_data2 == "Mixed - White And Black African"] <- "Mixed"
clean_ethnicity_data2[clean_ethnicity_data2 == "Mixed - White And Black Caribbean"] <- "Mixed"
clean_ethnicity_data2[clean_ethnicity_data2 == "Other Asian Background"] <- "Asian"
clean_ethnicity_data2[clean_ethnicity_data2 == "Other Black Background"] <- "Black"
clean_ethnicity_data2[clean_ethnicity_data2 == "Other Mixed Background"] <- "Mixed"

#remove not known,no information & info refused- as could be any ethnicity.
#also removed gypsy as only 1 student- not good for representation
#also removed other ethnic background- as not sure what category this relates to 

clean_ethnicity_data2<-clean_ethnicity_data2[!(clean_ethnicity_data2$Ethnicity=="Information Refused" | clean_ethnicity_data2$Ethnicity=="Information Refused - OBSOLETE VALUE" | clean_ethnicity_data2$Ethnicity=="No Information" | clean_ethnicity_data2$Ethnicity=="Not Known" | clean_ethnicity_data2$Ethnicity=="Gypsy or Traveller" | clean_ethnicity_data2$Ethnicity=="Other Ethnic Background" | clean_ethnicity_data2$Ethnicity=="Arab") ,]
```

```{r}
#converting to factors
clean_ethnicity_data2 %>%
  mutate_at(vars(Gender, Ethnicity), list(factor)) -> clean_ethnicity_data2

summary(clean_ethnicity_data2)
```

```{r}
#Ethnicity
#Percentage of students belonging to each ethnicity after categorisation
clean_ethnicity_data2 %>%
  group_by(Ethnicity) %>%
  summarise(counts = n()) %>%
  mutate(freq = (counts / sum(counts))) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot( ., aes(x="", y=freq, fill=Ethnicity)) +
  geom_bar(stat="identity", width=1, color="white") +
  coord_polar("y", start=0) +
  theme_void() +
  geom_text(aes(label = percentage), color = "white", position = position_stack(vjust = 0.5), size = 2.5) +
  scale_fill_brewer(palette = "Paired") +
  ggtitle("Percentage of students in each ethnic category") +
  theme(plot.title = element_text(hjust = 0.5)) 
```

Joining two datasets
```{r}
#JOIN ETHNIC DATA AND MARK DATA
inner_join(clean_ethnicity_data2, clean_mark_data) -> full_data2
print(distinct(full_data2))
```


## 2.1 Effect of Ethncicity (Non-White students) on student performance

```{r}
boxplot(MARK~Ethnicity, data = full_data2)
```

```{r}
#Visualise effect of ethnicity on overall/mean mark between 2017-2021
full_data2 %>%
  select(Ethnicity, MARK) %>%
  group_by(Ethnicity) %>%
  tally(mean(MARK))  %>%
  rename(Average_mark = n) %>%
  ggplot(aes(x=Ethnicity, y = Average_mark, fill =Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Paired") +
  geom_text(aes(label = round(Average_mark, 2)), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Average Test Scores by Ethnicity between 2017-2021") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ylab('Average Mark') 
```

```{r}
#looking at avg grade for each ethnicity type between 2017-2021
full_data2 %>%
  select(INSTANCE_ACADEMIC_YEAR, Ethnicity,MARK) %>%
  group_by(INSTANCE_ACADEMIC_YEAR, Ethnicity) %>%
  tally(mean(MARK)) %>%
  rename(Average_mark = n) %>%
  #spread(key = Ethnicity, value = Average_mark) %>%
  ggplot(aes(x = INSTANCE_ACADEMIC_YEAR, y = Average_mark, col = Ethnicity)) +
  geom_line() +
  geom_point() +
  ggtitle("Average grade for White vs BAME (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Academic Year') +
  ylab('Average Mark') +
  labs(col='Ethnicity') 
```

### Analysis of Non-white students Awarding Gap (2017-2021)

```{r}
#Visualising percentage of white vs BAME students achieving top degree (2017-2021)
full_data2 %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(Average_mark >=60) %>%
  select(ID2, Ethnicity, Average_mark) %>%
  distinct() %>%
  group_by(Ethnicity) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=Ethnicity, y = counts, fill =Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Pastel1") +
  geom_text(aes(label = percentage), position = position_stack(vjust = 1.1), size = 3)  +
  ggtitle("Percentage of white vs BAME students achieving top degree (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ylab('Number of students')
```

```{r}
#BAME AWARDING GAP
full_data2 %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  mutate(Grade = case_when(Average_mark >= 70 ~ "First Class",
                           Average_mark >= 60 ~ "Upper Second Class Honour",
                           Average_mark >= 50 ~ "Lower Second Class Honour",
                           Average_mark >= 40 ~ "Third class/pass",
                           Average_mark < 40 ~ "Fail")) %>%
  group_by(Ethnicity, Grade) %>%
  summarise(counts = n()) %>%
  mutate(freq = (counts / sum(counts))) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=Ethnicity, y = freq, fill =Grade)) +
  geom_bar(stat = 'identity') + 
  coord_flip() +
  scale_fill_brewer(palette = "Pastel1") +
  geom_text(aes(label = percentage), position = position_stack(vjust = 0.5), size = 2.5) +
  ggtitle("Distribution of degree outcomes by ethnic group (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  labs(y = "Frequency", fill = "Degree Classification")
```

## 2.2 Analysing effect of assessment type on non white students performance and awarding gap

```{r}
full_data2 %>%
  select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC,MARK) %>%
  group_by(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  tally(mean(MARK))  %>%
  rename(Average_mark = n) %>%
   #spread(key = Ethnicity, value = Average_mark) %>%
  ggplot(aes(x=ASSESSMENT_COMPONENT_TYPE_DESC, y = Average_mark, fill = Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge')  + 
  scale_fill_brewer(palette = "Set1") +
  geom_text(aes(label = round(Average_mark, 2)), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Average Mark By Assessment Type And Ethnicity") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Assessment Type') +
  ylab('Average Mark')
```

Effect of assessment type on awarding gap for Non-white categorise. 
```{r}
full_data2 %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(Average_mark >=60) %>%
  select(ID2, Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC, Average_mark) %>%
  distinct() %>%
  select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  group_by(Ethnicity,ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  #select(Ethnicity, ASSESSMENT_COMPONENT_TYPE_DESC, percentage) %>%
  ggplot(aes(x=ASSESSMENT_COMPONENT_TYPE_DESC, y = percentage, fill = Ethnicity)) +
  geom_bar(stat = 'identity', position = 'dodge')  + 
  scale_fill_brewer(palette = "Set1") +
  geom_text(aes(label = percentage), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Effect of assessment type on awarding gap") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Assessment Type') +
  ylab('Percentage of students')
```

```{r}
interaction.plot(x.factor = full_data2$Ethnicity, #x-axis variable
                 trace.factor = full_data2$ASSESSMENT_COMPONENT_TYPE_DESC, #variable for lines
                 response = full_data2$MARK, #y-axis variable
                 fun = median, #metric to plot
                 ylab = "Mark",
                 xlab = "Ethnicity",
                 col = c("pink", "lightblue"),
                 lty = 1, #line type
                 lwd = 2, #line width
                 trace.label = "Assessment type")
```

# Statistical Analysis

## Mixed Model 2
```{r}
#Question 2:
#	2.	What affect does the assessment type have on the performance of Non-White students (Namely Asian, Black, Chinese, and mixed)

mmodel2<- lmer(MARK ~ ASSESSMENT_COMPONENT_TYPE_DESC * Ethnicity + PROGRAMME_PART + (1|ID2) + (1|MODULE_CODE), data = full_data2)

```

```{r}
summary(mmodel2)
#Full model results
```

```{r}
#model 2 interaction plot
plot_model(mmodel2, type = "pred", terms = c("Ethnicity", "ASSESSMENT_COMPONENT_TYPE_DESC"))
```


```{r}
#Anova test
anova(mmodel2)
```

Similar to RQ1, all variables hold a significant effect on student performance

```{r}
#effect size
effectsize::eta_squared(mmodel2, partial = TRUE)
```

```{r}
#emmeans is meant to be looked at for main effect, in this case they were all significant so will do for each
emmeans(mmodel2, list(pairwise~ASSESSMENT_COMPONENT_TYPE_DESC), adjust="tukey")
```

```{r}
emmeans(mmodel2, list(pairwise~Ethnicity), adjust="tukey")
```

```{r}
emmeans(mmodel2, list(pairwise~PROGRAMME_PART), adjust="tukey")
```

```{r}
#There was an interaction so need emmeans for this
emmeans(mmodel2, pairwise ~ Ethnicity | ASSESSMENT_COMPONENT_TYPE_DESC)
```

## Evaluating mixed model 2 assumptions

### Homodscedasticity 

```{r}
#plot of mixed model 2
plot(mmodel2)
```

```{r}
boxplot(mmodel2@resp$wtres~full_data2$ASSESSMENT_COMPONENT_TYPE_DESC)
```

```{r}
boxplot(mmodel2@resp$wtres~full_data2$Ethnicity)
```

```{r}
boxplot(mmodel2@resp$wtres~full_data2$PROGRAMME_PART)
```
Another way to check for homeoscedasticity - (sd) residuals
```{r}
#ASSESSMENT TYPE
mmodel2res<-mmodel2@resp$wtres
#assessment type residuals in df
mmodel2df<-data.frame(full_data2$ASSESSMENT_COMPONENT_TYPE_DESC,mmodel2res) 
names(mmodel2df)<-c("ASSESSMENT_COMPONENT_TYPE_DESC","residual")
mmodel2df %>% group_by(ASSESSMENT_COMPONENT_TYPE_DESC)%>% summarise(sd(residual)) 
```
The assumption is met because highest sd is less than twice the lowest. 
```{r}
#Ethnicity
mmodel2df2<-data.frame(full_data2$Ethnicity,mmodel2res) 
names(mmodel2df2)<-c("Ethnicity","residual")
mmodel2df2 %>% group_by(Ethnicity)%>% summarise(sd(residual)) 
```
Again, the assumption is met. 

```{r}
#Programme part
mmodel2df3<-data.frame(full_data2$PROGRAMME_PART,mmodel2res) 
names(mmodel2df3)<-c("Programme_Part","residual")
mmodel2df3 %>% group_by(Programme_Part)%>% summarise(sd(residual))
```
Assumption satisfied again..

Moving onto checking the residuals of the model are normally distributed.

### Normality of residuals

```{r}
#now check for residuals for mmodel2
qqnorm(mmodel2res)
```
### Confidence Intervals

```{r}
#confint(mmodel2)
```

# **Research Question 3**
# **3. Is there a link between assessment type, gender and student achievement?**

Data set for this question will be full_data data set as already includes Gender data. Created this in preliminary analysis.

## 3.1 Effect of Gender on student performance

Distribution of Marks of Male & Female students (2017-2021)

```{r}
boxplot(MARK~Gender, data = full_data)
```
Below is bar chart showing of Gender of ethnicity on mean mark between 2017-2021

```{r}
#Visualise effect of ethnicity on overall/mean mark between 2017-2021
full_data %>%
  select(Gender, MARK) %>%
  group_by(Gender) %>%
  tally(mean(MARK))  %>%
  rename(Average_mark = n) %>%
  ggplot(aes(x=Gender, y = Average_mark, fill =Gender)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Paired") +
  geom_text(aes(label = round(Average_mark, 2)), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Average Test Scores by Gender between 2017-2021") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ylab('Average Mark') 
```

```{r}
#looking at avg grade for each ethnicity type between 2017-2021
full_data %>%
  select(INSTANCE_ACADEMIC_YEAR, Gender,MARK) %>%
  group_by(INSTANCE_ACADEMIC_YEAR, Gender) %>%
  tally(mean(MARK)) %>%
  rename(Average_mark = n) %>%
  #spread(key = Ethnicity, value = Average_mark) %>%
  ggplot(aes(x = INSTANCE_ACADEMIC_YEAR, y = Average_mark, col = Gender)) +
  geom_line() +
  geom_point() +
  ggtitle("Average grade for Men vs Women (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Academic Year') +
  ylab('Average Mark') +
  labs(col='Gender') 
```

### Analysis of Gender Awarding Gap (2017-2021)

```{r}
#Visualising percentage of white vs BAME students achieving top degree (2017-2021)
full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(MARK >=60) %>%
  select(ID2, Gender, Average_mark) %>%
  distinct() %>%
  group_by(Gender) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=Gender, y = counts, fill =Gender)) +
  geom_bar(stat = 'identity', position = 'dodge') + 
  scale_fill_brewer(palette = "Pastel1") +
  geom_text(aes(label = percentage), position = position_stack(vjust = 1.1), size = 3)  +
  ggtitle("Number of Men vs Women achieving top degree (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ylab('Number of students')
```
This is very interesting as more men are being awarded top degrees but on average woman perform better.
This may be because more men in this dataset then woman.
So, below is a better comparison, looking at distribution of grades.
```{r}
#Gender AWARDING GAP
full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  mutate(Grade = case_when(Average_mark >= 70 ~ "First Class",
                           Average_mark >= 60 ~ "Upper Second Class Honour",
                           Average_mark >= 50 ~ "Lower Second Class Honour",
                           Average_mark >= 40 ~ "Third class/pass",
                           Average_mark < 40 ~ "Fail")) %>%
  group_by(Gender, Grade) %>%
  summarise(counts = n()) %>%
  mutate(freq = (counts / sum(counts))) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  ggplot(aes(x=Gender, y = freq, fill =Grade)) +
  geom_bar(stat = 'identity') + 
  coord_flip() +
  scale_fill_brewer(palette = "Pastel1") +
  geom_text(aes(label = percentage), position = position_stack(vjust = 0.5), size = 2.5) +
  ggtitle("Distribution of degree outcomes by gender (2017-2021)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  labs(y = "Frequency", fill = "Degree Classification")
```
This graph is much better at showing what is actually going on in regards to what degree classification students are being awarded.
This makes more sense now...
This is showing 33.5% of woman students are getting a first in comparison to male students
So females do perform better/ more females are awarded a top degree in comparison to male students, but when looking at overall degree outcomes, more males are getting top degree because there are more male students at university.

## 3.2 Analysing effect of assessment type and gender on student performance and awarding gap

```{r}
full_data %>%
  select(Gender, ASSESSMENT_COMPONENT_TYPE_DESC,MARK) %>%
  group_by(Gender, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  tally(mean(MARK))  %>%
  rename(Average_mark = n) %>%
  ggplot(aes(x=ASSESSMENT_COMPONENT_TYPE_DESC, y = Average_mark, fill = Gender)) +
  geom_bar(stat = 'identity', position = 'dodge')  + 
  scale_fill_brewer(palette = "Set1") +
  geom_text(aes(label = round(Average_mark, 2)), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Average Mark By Assessment Type And Gender") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Assessment Type') +
  ylab('Average Mark')
```
This shows women perform better than men in both assessment types, but the difference by assessment type seems similar... So one gender does not perform significantly different than other in one assessment type. 

```{r}
  full_data %>%
  group_by(ID2) %>%
  mutate(Average_mark = mean(MARK)) %>%
  filter(Average_mark >=60) %>%
  select(ID2, Gender, ASSESSMENT_COMPONENT_TYPE_DESC, Average_mark) %>%
  distinct() %>%
  select(Gender, ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  group_by(Gender,ASSESSMENT_COMPONENT_TYPE_DESC) %>%
  summarise(counts = n()) %>%
  mutate(percentage = scales::label_percent()(counts / sum(counts))) %>%
  #select(Gender, ASSESSMENT_COMPONENT_TYPE_DESC, percentage) %>%
  ggplot(aes(x=ASSESSMENT_COMPONENT_TYPE_DESC, y = percentage, fill = Gender)) +
  geom_bar(stat = 'identity', position = 'dodge')  + 
  scale_fill_brewer(palette = "Set1") +
  geom_text(aes(label = percentage), position=position_dodge(width=0.9), vjust=-0.25) +
  ggtitle("Effect of assessment type on gender awarding gap") +
  theme(plot.title = element_text(hjust = 0.5)) +
  xlab('Assessment Type') +
  ylab('Percentage of students')
```
5% diff in each assessment type...
Doesnt look like exams are leading to a wider gender awarding gap. 
Difference in performance for exams is same as difference for coursework....

```{r}
interaction.plot(x.factor = full_data$Gender, #x-axis variable
                 trace.factor = full_data$ASSESSMENT_COMPONENT_TYPE_DESC, #variable for lines
                 response = full_data$MARK, #y-axis variable
                 fun = median, #metric to plot
                 ylab = "Mark",
                 xlab = "Ethnicity",
                 col = c("green", "blue"),
                 lty = 1, #line type
                 lwd = 2, #line width
                 trace.label = "Assessment type")
```

# Statistical Analysis

## Mixed Model 3

```{r}
# **Research Question 3**
# **3. Is there a link between assessment type, gender and student achievement?**

mmodel3<- lmer(MARK ~ ASSESSMENT_COMPONENT_TYPE_DESC * Gender + PROGRAMME_PART + (1|ID2) + (1|MODULE_CODE), data = full_data)
```

```{r}
summary(mmodel3)
```

This is interesting...shows gap in AT smaller for woman compared to men. 
Also average cw grade very similar for men and woman
And woman perform better in exams compared to men
And woman perform better in exams compared to men... 

```{r}
#interaction plot of mixed model 3
plot_model(mmodel3, type = "pred", terms = c("Gender", "ASSESSMENT_COMPONENT_TYPE_DESC"))
```

```{r}
anova(mmodel3)
```

Again, similar to RQ1 & RQ2, all variables hold a significant effect on student performance

```{r}
#effect size
effectsize::eta_squared(mmodel3, partial = TRUE)
```

```{r}
#emmeans is meant to be looked at for main effect in this case they were all significant so will do for each??

emmeans(mmodel3, list(pairwise~ASSESSMENT_COMPONENT_TYPE_DESC), adjust="tukey")
```

```{r}
emmeans(mmodel3, list(pairwise~Gender), adjust="tukey")
```

```{r}
emmeans(mmodel3, list(pairwise~PROGRAMME_PART), adjust="tukey")
```

```{r}
#There was an interaction so need emmeans for this
emmeans(mmodel3, pairwise ~ Gender | ASSESSMENT_COMPONENT_TYPE_DESC)
```

## Evaluating mixed model-3 assumptions

Do not need to check linearity as each variable is categorical.

### Homodscedasticity

```{r}
#plot of mixed model 3 residuals
plot(mmodel3)
```

```{r}
boxplot(mmodel3@resp$wtres~full_data$ASSESSMENT_COMPONENT_TYPE_DESC)
```

```{r}
boxplot(mmodel3@resp$wtres~full_data$Gender)
```

```{r}
boxplot(mmodel3@resp$wtres~full_data$PROGRAMME_PART)
```

Another way to check for homeoscedasticity - (sd) residuals

```{r}
#ASSESSMENT TYPE
mmodel3res<-mmodel3@resp$wtres
#assessment type residuals in df
mmodel3df<-data.frame(full_data$ASSESSMENT_COMPONENT_TYPE_DESC,mmodel3res) 
names(mmodel3df)<-c("ASSESSMENT_COMPONENT_TYPE_DESC","residual")
mmodel3df %>% group_by(ASSESSMENT_COMPONENT_TYPE_DESC)%>% summarise(sd(residual))
```

```{r}
#Gender
mmodel3df2<-data.frame(full_data$Gender,mmodel3res) 
names(mmodel3df2)<-c("Gender","residual")
mmodel3df2 %>% group_by(Gender)%>% summarise(sd(residual)) 
```

```{r}
#Programme part
mmodel3df3<-data.frame(full_data$PROGRAMME_PART,mmodel3res) 
names(mmodel3df3)<-c("Programme_Part","residual")
mmodel3df3 %>% group_by(Programme_Part)%>% summarise(sd(residual))
```
Assumptions met for all variables

### Normality of residuals

```{r}
qqnorm(mmodel3res)
```
All assumptions for mixed model 3 met. 

### Confidence Intervals

```{r}
#confint(mmodel3)
```

